Project acronym COMPECON
Project Complexity and Simplicity in Economic Mechanisms
Researcher (PI) Noam NISAN
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Advanced Grant (AdG), PE6, ERC-2016-ADG
Summary As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed
at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining
feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one.
A well-studied example of such situations is how the humble single-item auction suddenly becomes a
billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially
many possible outcomes. Similar complexity explosions occur with various markets, with information
dissemination, with pricing structures, and with many other economic mechanisms.
The aim of this proposal is to study the role and implications of such complexity and to start
developing a coherent economic theory that can handle it. We aim to identify various measures of
complexity that are crucial bottlenecks and study them. Examples of such complexities include the
amount of access to data, the length of the description of a mechanism, its communication requirements,
the cognitive complexity required from users, and, of course, the associated computational complexity.
On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on
the other hand, attempt avoiding complexity, when possible, replacing it with ``simple'' alternatives
without incurring too large of a loss.
Summary
As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed
at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining
feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one.
A well-studied example of such situations is how the humble single-item auction suddenly becomes a
billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially
many possible outcomes. Similar complexity explosions occur with various markets, with information
dissemination, with pricing structures, and with many other economic mechanisms.
The aim of this proposal is to study the role and implications of such complexity and to start
developing a coherent economic theory that can handle it. We aim to identify various measures of
complexity that are crucial bottlenecks and study them. Examples of such complexities include the
amount of access to data, the length of the description of a mechanism, its communication requirements,
the cognitive complexity required from users, and, of course, the associated computational complexity.
On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on
the other hand, attempt avoiding complexity, when possible, replacing it with ``simple'' alternatives
without incurring too large of a loss.
Max ERC Funding
2 026 706 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
Project acronym DeepFace
Project Understanding Deep Face Recognition
Researcher (PI) Lior Wolf
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Consolidator Grant (CoG), PE6, ERC-2016-COG
Summary Face recognition is a fascinating domain: no other domain seems to present as much value when analysing casual photos; it is one of the few domains in machine learning in which millions of classes are routinely learned; and the trade-off between subtle inter-identity variations and pronounced intra-identity variations forms a unique challenge.
The advent of deep learning has brought machines to what is considered a human level of performance. However, there are many research questions that are left open. At the top most level, we ask two questions: what is unique about faces in comparison to other recognition tasks that also employ deep networks and how can we make the next leap in performance of automatic face recognition?
We consider three domains of research. The first is the study of methods that promote effective transfer learning. This is crucial since all state of the art face recognition methods rely on transfer learning. The second domain is the study of the tradeoffs that govern the optimal utilization of the training data and how the properties of the training data affect the optimal network design. The third domain is the post transfer utilization of the learned deep networks, where given the representations of a pair of face images, we seek to compare them in the most accurate way.
Throughout this proposal, we put an emphasis on theoretical reasoning. I aim to support the developed methods by a theoretical framework that would both justify their usage as well as provide concrete guidelines for using them. My goal of achieving a leap forward in performance through a level of theoretical analysis that is unparalleled in object recognition, makes our research agenda truly high-risk/ high-gains. I have been in the forefront of face recognition for the last 8 years and my lab's recent achievements in deep learning suggest that we will be able to carry out this research. To further support its feasibility, we present very promising initial results.
Summary
Face recognition is a fascinating domain: no other domain seems to present as much value when analysing casual photos; it is one of the few domains in machine learning in which millions of classes are routinely learned; and the trade-off between subtle inter-identity variations and pronounced intra-identity variations forms a unique challenge.
The advent of deep learning has brought machines to what is considered a human level of performance. However, there are many research questions that are left open. At the top most level, we ask two questions: what is unique about faces in comparison to other recognition tasks that also employ deep networks and how can we make the next leap in performance of automatic face recognition?
We consider three domains of research. The first is the study of methods that promote effective transfer learning. This is crucial since all state of the art face recognition methods rely on transfer learning. The second domain is the study of the tradeoffs that govern the optimal utilization of the training data and how the properties of the training data affect the optimal network design. The third domain is the post transfer utilization of the learned deep networks, where given the representations of a pair of face images, we seek to compare them in the most accurate way.
Throughout this proposal, we put an emphasis on theoretical reasoning. I aim to support the developed methods by a theoretical framework that would both justify their usage as well as provide concrete guidelines for using them. My goal of achieving a leap forward in performance through a level of theoretical analysis that is unparalleled in object recognition, makes our research agenda truly high-risk/ high-gains. I have been in the forefront of face recognition for the last 8 years and my lab's recent achievements in deep learning suggest that we will be able to carry out this research. To further support its feasibility, we present very promising initial results.
Max ERC Funding
1 696 888 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
Project acronym DELPHI
Project Computing Answers to Complex Questions in Broad Domains
Researcher (PI) Jonathan Berant
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), PE6, ERC-2018-STG
Summary The explosion of information around us has democratized knowledge and transformed its availability for
people around the world. Still, since information is mediated through automated systems, access is bounded
by their ability to understand language.
Consider an economist asking “What fraction of the top-5 growing countries last year raised their co2 emission?”.
While the required information is available, answering such complex questions automatically is
not possible. Current question answering systems can answer simple questions in broad domains, or complex
questions in narrow domains. However, broad and complex questions are beyond the reach of state-of-the-art.
This is because systems are unable to decompose questions into their parts, and find the relevant information
in multiple sources. Further, as answering such questions is hard for people, collecting large datasets to train
such models is prohibitive.
In this proposal I ask: Can computers answer broad and complex questions that require reasoning over
multiple modalities? I argue that by synthesizing the advantages of symbolic and distributed representations
the answer will be “yes”. My thesis is that symbolic representations are suitable for meaning composition, as
they provide interpretability, coverage, and modularity. Complementarily, distributed representations (learned
by neural nets) excel at capturing the fuzziness of language. I propose a framework where complex questions
are symbolically decomposed into sub-questions, each is answered with a neural network, and the final answer
is computed from all gathered information.
This research tackles foundational questions in language understanding. What is the right representation
for reasoning in language? Can models learn to perform complex actions in the face of paucity of data?
Moreover, my research, if successful, will transform how we interact with machines, and define a role for
them as research assistants in science, education, and our daily life.
Summary
The explosion of information around us has democratized knowledge and transformed its availability for
people around the world. Still, since information is mediated through automated systems, access is bounded
by their ability to understand language.
Consider an economist asking “What fraction of the top-5 growing countries last year raised their co2 emission?”.
While the required information is available, answering such complex questions automatically is
not possible. Current question answering systems can answer simple questions in broad domains, or complex
questions in narrow domains. However, broad and complex questions are beyond the reach of state-of-the-art.
This is because systems are unable to decompose questions into their parts, and find the relevant information
in multiple sources. Further, as answering such questions is hard for people, collecting large datasets to train
such models is prohibitive.
In this proposal I ask: Can computers answer broad and complex questions that require reasoning over
multiple modalities? I argue that by synthesizing the advantages of symbolic and distributed representations
the answer will be “yes”. My thesis is that symbolic representations are suitable for meaning composition, as
they provide interpretability, coverage, and modularity. Complementarily, distributed representations (learned
by neural nets) excel at capturing the fuzziness of language. I propose a framework where complex questions
are symbolically decomposed into sub-questions, each is answered with a neural network, and the final answer
is computed from all gathered information.
This research tackles foundational questions in language understanding. What is the right representation
for reasoning in language? Can models learn to perform complex actions in the face of paucity of data?
Moreover, my research, if successful, will transform how we interact with machines, and define a role for
them as research assistants in science, education, and our daily life.
Max ERC Funding
1 499 375 €
Duration
Start date: 2019-04-01, End date: 2024-03-31
Project acronym DIFFOP
Project Nonlinear Data and Signal Analysis with Diffusion Operators
Researcher (PI) Ronen TALMON
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Starting Grant (StG), PE6, ERC-2018-STG
Summary Nowadays, extensive collection and storage of massive data sets have become a routine in multiple disciplines and in everyday life. These large amounts of intricate data often make data samples arithmetic and basic comparisons problematic, raising new challenges to traditional data analysis objectives such as filtering and prediction. Furthermore, the availability of such data constantly pushes the boundaries of data analysis to new emerging domains, ranging from neuronal and social network analysis to multimodal sensor fusion. The combination of evolved data and new domains drives a fundamental change in the field of data analysis. Indeed, many classical model-based techniques have become obsolete since their models do not embody the richness of the collected data. Today, one notable avenue of research is the development of nonlinear techniques that transition from data to creating representations, without deriving models in closed-form. The vast majority of such existing data-driven methods operate directly on the data, a hard task by itself when the data are large and elaborated. The goal of this research is to develop a fundamentally new methodology for high dimensional data analysis with diffusion operators, making use of recent transformative results in manifold and geometry learning. More concretely, shifting the focus from processing the data samples themselves and considering instead structured data through the lens of diffusion operators will introduce new powerful “handles” to data, capturing their complexity efficiently. We will study the basic theory behind this nonlinear analysis, develop new operators for this purpose, and devise efficient data-driven algorithms. In addition, we will explore how our approach can be leveraged for devising efficient solutions to a broad range of open real-world data analysis problems, involving intrinsic representations, sensor fusion, time-series analysis, network connectivity inference, and domain adaptation.
Summary
Nowadays, extensive collection and storage of massive data sets have become a routine in multiple disciplines and in everyday life. These large amounts of intricate data often make data samples arithmetic and basic comparisons problematic, raising new challenges to traditional data analysis objectives such as filtering and prediction. Furthermore, the availability of such data constantly pushes the boundaries of data analysis to new emerging domains, ranging from neuronal and social network analysis to multimodal sensor fusion. The combination of evolved data and new domains drives a fundamental change in the field of data analysis. Indeed, many classical model-based techniques have become obsolete since their models do not embody the richness of the collected data. Today, one notable avenue of research is the development of nonlinear techniques that transition from data to creating representations, without deriving models in closed-form. The vast majority of such existing data-driven methods operate directly on the data, a hard task by itself when the data are large and elaborated. The goal of this research is to develop a fundamentally new methodology for high dimensional data analysis with diffusion operators, making use of recent transformative results in manifold and geometry learning. More concretely, shifting the focus from processing the data samples themselves and considering instead structured data through the lens of diffusion operators will introduce new powerful “handles” to data, capturing their complexity efficiently. We will study the basic theory behind this nonlinear analysis, develop new operators for this purpose, and devise efficient data-driven algorithms. In addition, we will explore how our approach can be leveraged for devising efficient solutions to a broad range of open real-world data analysis problems, involving intrinsic representations, sensor fusion, time-series analysis, network connectivity inference, and domain adaptation.
Max ERC Funding
1 260 000 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym FAFC
Project Foundations and Applications of Functional Cryptography
Researcher (PI) Gil SEGEV
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Starting Grant (StG), PE6, ERC-2016-STG
Summary "Modern cryptography has successfully followed an ""all-or-nothing"" design paradigm over the years. For example, the most fundamental task of data encryption requires that encrypted data be fully recoverable using the encryption key, but be completely useless without it. Nowadays, however, this paradigm is insufficient for a wide variety of evolving applications, and a more subtle approach is urgently needed. This has recently motivated the cryptography community to put forward a vision of ""functional cryptography'': Designing cryptographic primitives that allow fine-grained access to sensitive data.
This proposal aims at making substantial progress towards realizing the premise of functional cryptography. By tackling challenging key problems in both the foundations and the applications of functional cryptography, I plan to direct the majority of our effort towards addressing the following three fundamental objectives, which span a broad and interdisciplinary flavor of research directions: (1) Obtain a better understanding of functional cryptography's building blocks, (2) develop functional cryptographic tools and schemes based on well-studied assumptions, and (3) increase the usability of functional cryptographic systems via algorithmic techniques.
Realizing the premise of functional cryptography is of utmost importance not only to the development of modern cryptography, but in fact to our entire technological development, where fine-grained access to sensitive data plays an instrumental role. Moreover, our objectives are tightly related to two of the most fundamental open problems in cryptography: Basing cryptography on widely-believed worst-case complexity assumptions, and basing public-key cryptography on private-key primitives. I strongly believe that meaningful progress towards achieving our objectives will shed new light on these key problems, and thus have a significant impact on our understanding of modern cryptography."
Summary
"Modern cryptography has successfully followed an ""all-or-nothing"" design paradigm over the years. For example, the most fundamental task of data encryption requires that encrypted data be fully recoverable using the encryption key, but be completely useless without it. Nowadays, however, this paradigm is insufficient for a wide variety of evolving applications, and a more subtle approach is urgently needed. This has recently motivated the cryptography community to put forward a vision of ""functional cryptography'': Designing cryptographic primitives that allow fine-grained access to sensitive data.
This proposal aims at making substantial progress towards realizing the premise of functional cryptography. By tackling challenging key problems in both the foundations and the applications of functional cryptography, I plan to direct the majority of our effort towards addressing the following three fundamental objectives, which span a broad and interdisciplinary flavor of research directions: (1) Obtain a better understanding of functional cryptography's building blocks, (2) develop functional cryptographic tools and schemes based on well-studied assumptions, and (3) increase the usability of functional cryptographic systems via algorithmic techniques.
Realizing the premise of functional cryptography is of utmost importance not only to the development of modern cryptography, but in fact to our entire technological development, where fine-grained access to sensitive data plays an instrumental role. Moreover, our objectives are tightly related to two of the most fundamental open problems in cryptography: Basing cryptography on widely-believed worst-case complexity assumptions, and basing public-key cryptography on private-key primitives. I strongly believe that meaningful progress towards achieving our objectives will shed new light on these key problems, and thus have a significant impact on our understanding of modern cryptography."
Max ERC Funding
1 307 188 €
Duration
Start date: 2017-02-01, End date: 2022-01-31
Project acronym FTHPC
Project Fault Tolerant High Performance Computing
Researcher (PI) Oded Schwartz
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Consolidator Grant (CoG), PE6, ERC-2018-COG
Summary Supercomputers are strategically crucial for facilitating advances in science and technology: in climate change research, accelerated genome sequencing towards cancer treatments, cutting edge physics, devising engineering innovative solutions, and many other compute intensive problems. However, the future of super-computing depends on our ability to cope with the ever increasing rate of faults (bit flips and component failure), resulting from the steadily increasing machine size and decreasing operating voltage. Indeed, hardware trends predict at least two faults per minute for next generation (exascale) supercomputers.
The challenge of ascertaining fault tolerance for high-performance computing is not new, and has been the focus of extensive research for over two decades. However, most solutions are either (i) general purpose, requiring little to no algorithmic effort, but severely degrading performance (e.g., checkpoint-restart), or (ii) tailored to specific applications and very efficient, but requiring high expertise and significantly increasing programmers' workload. We seek the best of both worlds: high performance and general purpose fault resilience.
Efficient general purpose solutions (e.g., via error correcting codes) have revolutionized memory and communication devices over two decades ago, enabling programmers to effectively disregard the very
likely memory and communication errors. The time has come for a similar paradigm shift in the computing regimen. I argue that exciting recent advances in error correcting codes, and in short probabilistically checkable proofs, make this goal feasible. Success along these lines will eliminate the bottleneck of required fault-tolerance expertise, and open exascale computing to all algorithm designers and programmers, for the benefit of the scientific, engineering, and industrial communities.
Summary
Supercomputers are strategically crucial for facilitating advances in science and technology: in climate change research, accelerated genome sequencing towards cancer treatments, cutting edge physics, devising engineering innovative solutions, and many other compute intensive problems. However, the future of super-computing depends on our ability to cope with the ever increasing rate of faults (bit flips and component failure), resulting from the steadily increasing machine size and decreasing operating voltage. Indeed, hardware trends predict at least two faults per minute for next generation (exascale) supercomputers.
The challenge of ascertaining fault tolerance for high-performance computing is not new, and has been the focus of extensive research for over two decades. However, most solutions are either (i) general purpose, requiring little to no algorithmic effort, but severely degrading performance (e.g., checkpoint-restart), or (ii) tailored to specific applications and very efficient, but requiring high expertise and significantly increasing programmers' workload. We seek the best of both worlds: high performance and general purpose fault resilience.
Efficient general purpose solutions (e.g., via error correcting codes) have revolutionized memory and communication devices over two decades ago, enabling programmers to effectively disregard the very
likely memory and communication errors. The time has come for a similar paradigm shift in the computing regimen. I argue that exciting recent advances in error correcting codes, and in short probabilistically checkable proofs, make this goal feasible. Success along these lines will eliminate the bottleneck of required fault-tolerance expertise, and open exascale computing to all algorithm designers and programmers, for the benefit of the scientific, engineering, and industrial communities.
Max ERC Funding
1 824 467 €
Duration
Start date: 2019-06-01, End date: 2024-05-31
Project acronym HARMONIC
Project Discrete harmonic analysis for computer science
Researcher (PI) Yuval FILMUS
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Starting Grant (StG), PE6, ERC-2018-STG
Summary Boolean function analysis is a topic of research at the heart of theoretical computer science. It studies functions on n input bits (for example, functions computed by Boolean circuits) from a spectral perspective, by treating them as real-valued functions on the group Z_2^n, and using techniques from Fourier and functional analysis. Boolean function analysis has been applied to a wide variety of areas within theoretical computer science, including hardness of approximation, learning theory, coding theory, and quantum complexity theory.
Despite its immense usefulness, Boolean function analysis has limited scope, since it is only appropriate for studying functions on {0,1}^n (a domain known as the Boolean hypercube). Discrete harmonic analysis is the study of functions on domains possessing richer algebraic structure such as the symmetric group (the group of all permutations), using techniques from representation theory and Sperner theory. The considerable success of Boolean function analysis suggests that discrete harmonic analysis could likewise play a central role in theoretical computer science.
The goal of this proposal is to systematically develop discrete harmonic analysis on a broad variety of domains, with an eye toward applications in several areas of theoretical computer science. We will generalize classical results of Boolean function analysis beyond the Boolean hypercube, to domains such as finite groups, association schemes (a generalization of finite groups), the quantum analog of the Boolean hypercube, and high-dimensional expanders (high-dimensional analogs of expander graphs). Potential applications include a quantum PCP theorem and two outstanding open questions in hardness of approximation: the Unique Games Conjecture and the Sliding Scale Conjecture. Beyond these concrete applications, we expect that the fundamental results we prove will have many other applications that are hard to predict in advance.
Summary
Boolean function analysis is a topic of research at the heart of theoretical computer science. It studies functions on n input bits (for example, functions computed by Boolean circuits) from a spectral perspective, by treating them as real-valued functions on the group Z_2^n, and using techniques from Fourier and functional analysis. Boolean function analysis has been applied to a wide variety of areas within theoretical computer science, including hardness of approximation, learning theory, coding theory, and quantum complexity theory.
Despite its immense usefulness, Boolean function analysis has limited scope, since it is only appropriate for studying functions on {0,1}^n (a domain known as the Boolean hypercube). Discrete harmonic analysis is the study of functions on domains possessing richer algebraic structure such as the symmetric group (the group of all permutations), using techniques from representation theory and Sperner theory. The considerable success of Boolean function analysis suggests that discrete harmonic analysis could likewise play a central role in theoretical computer science.
The goal of this proposal is to systematically develop discrete harmonic analysis on a broad variety of domains, with an eye toward applications in several areas of theoretical computer science. We will generalize classical results of Boolean function analysis beyond the Boolean hypercube, to domains such as finite groups, association schemes (a generalization of finite groups), the quantum analog of the Boolean hypercube, and high-dimensional expanders (high-dimensional analogs of expander graphs). Potential applications include a quantum PCP theorem and two outstanding open questions in hardness of approximation: the Unique Games Conjecture and the Sliding Scale Conjecture. Beyond these concrete applications, we expect that the fundamental results we prove will have many other applications that are hard to predict in advance.
Max ERC Funding
1 473 750 €
Duration
Start date: 2019-03-01, End date: 2024-02-29
Project acronym HOLI
Project Deep Learning for Holistic Inference
Researcher (PI) Amir Globerson
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Consolidator Grant (CoG), PE6, ERC-2018-COG
Summary Machine learning has rapidly evolved in the last decade, significantly improving accuracy on tasks such as image classification. Much of this success can be attributed to the re-emergence of neural nets. However, learning algorithms are still far from achieving the capabilities of human cognition. In particular, humans can rapidly organize an input stream (e.g., textual or visual) into a set of entities, and understand the complex relations between those. In this project I aim to create a general methodology for semantic interpretation of input streams. Such problems fall under the structured-prediction framework, to which I have made numerous contributions. The proposal identifies and addresses three key components required for a comprehensive and empirically effective approach to the problem.
First, we consider the holistic nature of semantic interpretations, where a top-down process chooses a coherent interpretation among the vast number of options. We argue that deep-learning architectures are ideally suited for modeling such coherence scores, and propose to develop the corresponding theory and algorithms. Second, we address the complexity of the semantic representation, where a stream is mapped into a variable number of entities, each having multiple attributes and relations to other entities. We characterize the properties a model should satisfy in order to produce such interpretations, and propose novel models that achieve this. Third, we develop a theory for understanding when such models can be learned efficiently, and how well they can generalize. To achieve this, we address key questions of non-convex optimization, inductive bias and generalization. We expect these contributions to have a dramatic impact on AI systems, from machine reading of text to image analysis. More broadly, they will help bridge the gap between machine learning as an engineering field, and the study of human cognition.
Summary
Machine learning has rapidly evolved in the last decade, significantly improving accuracy on tasks such as image classification. Much of this success can be attributed to the re-emergence of neural nets. However, learning algorithms are still far from achieving the capabilities of human cognition. In particular, humans can rapidly organize an input stream (e.g., textual or visual) into a set of entities, and understand the complex relations between those. In this project I aim to create a general methodology for semantic interpretation of input streams. Such problems fall under the structured-prediction framework, to which I have made numerous contributions. The proposal identifies and addresses three key components required for a comprehensive and empirically effective approach to the problem.
First, we consider the holistic nature of semantic interpretations, where a top-down process chooses a coherent interpretation among the vast number of options. We argue that deep-learning architectures are ideally suited for modeling such coherence scores, and propose to develop the corresponding theory and algorithms. Second, we address the complexity of the semantic representation, where a stream is mapped into a variable number of entities, each having multiple attributes and relations to other entities. We characterize the properties a model should satisfy in order to produce such interpretations, and propose novel models that achieve this. Third, we develop a theory for understanding when such models can be learned efficiently, and how well they can generalize. To achieve this, we address key questions of non-convex optimization, inductive bias and generalization. We expect these contributions to have a dramatic impact on AI systems, from machine reading of text to image analysis. More broadly, they will help bridge the gap between machine learning as an engineering field, and the study of human cognition.
Max ERC Funding
1 932 500 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym iEXTRACT
Project Information Extraction for Everyone
Researcher (PI) Yoav Goldberg
Host Institution (HI) BAR ILAN UNIVERSITY
Call Details Starting Grant (StG), PE6, ERC-2018-STG
Summary Staggering amounts of information are stored in natural language documents, rendering them unavailable to data-science techniques. Information Extraction (IE), a subfield of Natural Language Processing (NLP), aims to automate the extraction of structured information from text, yielding datasets that can be queried, analyzed and combined to provide new insights and drive research forward.
Despite tremendous progress in NLP, IE systems remain mostly inaccessible to non-NLP-experts who can greatly benefit from them. This stems from the current methods for creating IE systems: the dominant machine-learning (ML) approach requires technical expertise and large amounts of annotated data, and does not provide the user control over the extraction process. The previously dominant rule-based approach unrealistically requires the user to anticipate and deal with the nuances of natural language.
I aim to remedy this situation by revisiting rule-based IE in light of advances in NLP and ML. The key idea is to cast IE as a collaborative human-computer effort, in which the user provides domain-specific knowledge, and the system is in charge of solving various domain-independent linguistic complexities, ultimately allowing the user to query
unstructured texts via easily structured forms.
More specifically, I aim develop:
(a) a novel structured representation that abstracts much of the complexity of natural language;
(b) algorithms that derive these representations from texts;
(c) an accessible rule language to query this representation;
(d) AI components that infer the user extraction intents, and based on them promote relevant examples and highlight extraction cases that require special attention.
The ultimate goal of this project is to democratize NLP and bring advanced IE capabilities directly to the hands of
domain-experts: doctors, lawyers, researchers and scientists, empowering them to process large volumes of data and
advance their profession.
Summary
Staggering amounts of information are stored in natural language documents, rendering them unavailable to data-science techniques. Information Extraction (IE), a subfield of Natural Language Processing (NLP), aims to automate the extraction of structured information from text, yielding datasets that can be queried, analyzed and combined to provide new insights and drive research forward.
Despite tremendous progress in NLP, IE systems remain mostly inaccessible to non-NLP-experts who can greatly benefit from them. This stems from the current methods for creating IE systems: the dominant machine-learning (ML) approach requires technical expertise and large amounts of annotated data, and does not provide the user control over the extraction process. The previously dominant rule-based approach unrealistically requires the user to anticipate and deal with the nuances of natural language.
I aim to remedy this situation by revisiting rule-based IE in light of advances in NLP and ML. The key idea is to cast IE as a collaborative human-computer effort, in which the user provides domain-specific knowledge, and the system is in charge of solving various domain-independent linguistic complexities, ultimately allowing the user to query
unstructured texts via easily structured forms.
More specifically, I aim develop:
(a) a novel structured representation that abstracts much of the complexity of natural language;
(b) algorithms that derive these representations from texts;
(c) an accessible rule language to query this representation;
(d) AI components that infer the user extraction intents, and based on them promote relevant examples and highlight extraction cases that require special attention.
The ultimate goal of this project is to democratize NLP and bring advanced IE capabilities directly to the hands of
domain-experts: doctors, lawyers, researchers and scientists, empowering them to process large volumes of data and
advance their profession.
Max ERC Funding
1 499 354 €
Duration
Start date: 2019-05-01, End date: 2024-04-30
Project acronym ImmuneCheckpointsAD
Project Immune checkpoint blockade for fighting Alzheimer’s disease
Researcher (PI) Michal EISENBACH-SCHWARTZ
Host Institution (HI) WEIZMANN INSTITUTE OF SCIENCE
Call Details Advanced Grant (AdG), LS5, ERC-2016-ADG
Summary Understanding, and ultimately treating Alzheimer’s disease (AD) is a major need in Western countries. Currently, there is no available treatment to modify the disease. Several pioneering discoveries made by my team, attributing a key role to systemic immunity in brain maintenance and repair, and identifying unique interface between the brain’s borders through which the immune system assists the brain, led us to our recent discovery that transient reduction of systemic immune suppression could modify disease pathology, and reverse cognitive loss in mouse models of AD (Nature Communications, 2015; Nature Medicine, 2016; Science, 2014). This discovery emphasizes that AD is not restricted to the brain, but is associated with systemic immune dysfunction. Thus, the goal of addressing numerous risk factors that go awry in the AD brain, many of which are -as yet- unknown, could be accomplished by immunotherapy, using immune checkpoint blockade directed at the Programmed-death (PD)-1 pathway, to empower the immune system. In this proposal, we will adopt our new experimental paradigm to discover mechanisms through which the immune system supports the brain, and to identify key/novel molecular and cellular processes at various stages of the disease that are responsible for cognitive decline long before neurons are lost, and whose reversal or modification is needed to mitigate AD pathology, and prevent cognitive loss. Achieving our goals requires the multidisciplinary approaches and expertise at our disposal, including state-of-the art immunological, cellular, molecular, and genomic tools. The results will pave the way for developing a novel next-generation immunotherapy, by targeting additional selective immune checkpoint pathways, or identifying a specific immune-based therapeutic target, for prevention and treatment of AD. We expect that our results will help attain the ultimate goal of converting an escalating untreatable disease into a chronic treatable one.
Summary
Understanding, and ultimately treating Alzheimer’s disease (AD) is a major need in Western countries. Currently, there is no available treatment to modify the disease. Several pioneering discoveries made by my team, attributing a key role to systemic immunity in brain maintenance and repair, and identifying unique interface between the brain’s borders through which the immune system assists the brain, led us to our recent discovery that transient reduction of systemic immune suppression could modify disease pathology, and reverse cognitive loss in mouse models of AD (Nature Communications, 2015; Nature Medicine, 2016; Science, 2014). This discovery emphasizes that AD is not restricted to the brain, but is associated with systemic immune dysfunction. Thus, the goal of addressing numerous risk factors that go awry in the AD brain, many of which are -as yet- unknown, could be accomplished by immunotherapy, using immune checkpoint blockade directed at the Programmed-death (PD)-1 pathway, to empower the immune system. In this proposal, we will adopt our new experimental paradigm to discover mechanisms through which the immune system supports the brain, and to identify key/novel molecular and cellular processes at various stages of the disease that are responsible for cognitive decline long before neurons are lost, and whose reversal or modification is needed to mitigate AD pathology, and prevent cognitive loss. Achieving our goals requires the multidisciplinary approaches and expertise at our disposal, including state-of-the art immunological, cellular, molecular, and genomic tools. The results will pave the way for developing a novel next-generation immunotherapy, by targeting additional selective immune checkpoint pathways, or identifying a specific immune-based therapeutic target, for prevention and treatment of AD. We expect that our results will help attain the ultimate goal of converting an escalating untreatable disease into a chronic treatable one.
Max ERC Funding
2 287 500 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
