Learning Club
A Theoretical Analysis of Generalization in Graph Convolutional Neural Networks – Ron Levie from Ludwig Maximilian University of Munich
Recording: https://us02web.zoom.us/rec/share/fHCCma3ibcwJDDysXJeiVXwJgIrZB2y6KP70r0lSoVx6f66BFk00LyWd7af-fPpy.RD5WoGdSZW92a31V?startTime=1638093861000 --------------------------------------------------------------------- Hey Everyone, On Sunday 28.11.2021, we will host Ron Levie from Ludwig Maximilian University of Munich. Zoom: https://us02web.zoom.us/j/86540744550?pwd=bTlWMnZSWU1KcHFaSVlaUmR3aDNNUT09 Meeting ID: 865 4074 4550 Passcode: 748585 Title A Theoretical Analysis of Generalization in Graph Convolutional Neural Networks Abstract In recent years, the need to accommodate non-Euclidean structures in data science has brought a ... Read more
Equivariant Subgraph Aggregation Networks – Haggai Maron (NVIDIA Research)
The recording from Haggai's talk: https://us02web.zoom.us/rec/share/jCgJIjXL_UM5KV2z7NwgL3ij2SuLvrTLmVGF4OkI9Vk_J4RFfgYx79h9iC7Z41XQ.3FksbTtcNCIk-phG?startTime=1639303317000 On Sunday 12.12.2021, we will host Haggai Maron from NVIDIA Research. Zoom: https://us02web.zoom.us/j/85743049730?pwd=dVNRcVBJRlJxaTkrZzZkdSt1ajlUQT09 Meeting ID: 857 4304 9730 Passcode: 902259 Title: Equivariant Subgraph Aggregation Networks Abstract: Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. ... Read more
Generalization in RL – A Bayesian Perspective (Aviv Tamar, Technion)
On Sunday 19.12.2021, we will host Aviv Tamar from Technion. Zoom https://us02web.zoom.us/j/85352616833?pwd=ZDBQNVBESFg2cERlalRhbWNrbzIrQT09 Meeting ID: 853 5261 6833 Passcode: 897342 Title Generalization in RL - A Bayesian Perspective Abstract How can an agent learn to quickly perform well in an unknown task? This is the basic question in reinforcement learning (RL), and is critical for any ... Read more
The implicit bias of SGD: Minima stability analysis – Tomer Michaeli, Technion
The recording of the talk: https://us02web.zoom.us/rec/share/kIED6jkl9xH8y8pzyHAlYig11X11pbDjBqBsijV1kvrzIeOnElEr1ruFhTA1GVLN.vsmhBZvDEjvRx0Kc?startTime=1640513140000 Zoom https://us02web.zoom.us/j/83447514190pwd=cDVsc3Y0dWJOTFIwNlhSQmV5Vkpudz09 Meeting ID: 834 4751 4190 Passcode: 1212 Title The implicit bias of SGD: Minima stability analysis Abstract One of the puzzling phenomena in deep learning, is that neural networks tend to generalize well even when they are highly overparameterized. This stands in sharp contrast to classical wisdom, ... Read more
Learning with fewer labels in Computer Vision – Amir Bar – TAU
The recording of Amir's talk: https://us02web.zoom.us/rec/play/eErrSv8QOE9pC0bNNU4cB46ZiUXuKwbaEXv01MHvyFYTcwikE3Tb4FNob3A00Fi-6csubiImwmcE2OIl.sfukRxaffBiRikPo?startTime=1641117754000 Learning Club BIU - Amir Bar - TAU When Sun, January 2, 2022, 12pm – 1pm Zoom https://us02web.zoom.us/j/83447514190?pwd=cDVsc3Y0dWJOTFIwNlhSQmV5Vkpudz09 Meeting ID: 834 4751 4190 Passcode: 1212 Title: Learning with fewer labels in Computer Vision Abstract: In recent years, deep neural networks have transformed the field of Computer Vision. Current neural ... Read more
Learning Club – Itai Lang – TAU – Geometric Adversarial Attacks and Defenses on 3D Point Clouds
Learning Club - Itai Lang - TAU - Geometric Adversarial Attacks and Defenses on 3D Point Clouds When: Sun, January 9, 2022, 12pm – 1pm Title: Geometric Adversarial Attacks and Defenses on 3D Point Clouds Abstract: Deep neural networks are prone to adversarial examples that maliciously alter the network's outcome. Due to the increasing popularity ... Read more
Continuous vs. Discrete Optimization of Deep Neural Networks. Nadav Cohen (TAU).
On Sunday 6.3.22 12:00 we will host Nadav Cohen from Tel-Aviv University. Please see the details below. The recording of Nadav' talk: https://us02web.zoom.us/rec/share/cNPxCTCpoLjYZFQI0MMiiukHgNypjRiBkAEobfovvw-ebJzaF1r0RNDpXbfZhdIJ.wmI2PSdk1z3fGxyu?startTime=1646561098000 Title: Continuous vs. Discrete Optimization of Deep Neural Networks Abstract: Existing analyses of optimization in deep learning are either continuous, focusing on variants of gradient flow (GF), or discrete, directly treating variants ... Read more
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators. Rinon Gal (TAU and NVIDIA).
Zoom: https://us02web.zoom.us/j/85784376223?pwd=VkMvdGl1YXFBMExSdC9mRnVnZjZIQT09 Meeting ID: 857 8437 6223 Passcode: 1212 Rinon Gal from Tel-Aviv university and NVIDIA. Title: StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators Abstract: Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator ... Read more
DeepDPM: Deep Clustering With an Unknown Number of Clusters. Meitar Ronen (Ben-Gurion University)
The recording of Meitar's talk is available: https://us02web.zoom.us/rec/share/mdFnT_lf6ct-9VRfTFhnrsEe9724KteEsl7v8I86zf19AaDtKiChgM3Ai6zxQ5CG.PcEfwNZ04_H-NsGm?startTime=1650790948000 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ On Sunday 24.4.22, at 12:00, we will host Meitar Ronen from Ben-Gurion University. Zoom: https://us02web.zoom.us/j/85784376223?pwd=VkMvdGl1YXFBMExSdC9mRnVnZjZIQT09 Meeting ID: 857 8437 6223 Passcode: 1212 Title: DeepDPM: Deep Clustering With an Unknown Number of Clusters. Abstract: Deep Learning (DL) has shown great promise in the unsupervised task of clustering. ... Read more
Label-free Domain Adaptation and Multimodal Manifold Learning with Riemannian Geometry. Ronen Talmon (Technion)
The recording of Ronen's talk is available at the following link: https://us02web.zoom.us/rec/share/CyXhXnADPm2gwEPqa4NDasRx3Zz_1DOUMR8LgAiBmO6uPy3JB7ydmXXhorZV55RP.xQTltElplVGiOVcR?startTime=1651395685000 Title: Label-free Domain Adaptation and Multimodal Manifold Learning with Riemannian Geometry Abstract: Recently, Riemannian geometry has become a central ingredient in a broad range of data analysis and learning tools. Broadly, it facilitates features of complex high-dimensional data with a known non-Euclidean geometry. In this ... Read more
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Mahmood Sharif Guest Lecture From TAU – Learning Club 12.6.22
###### Meeting Info ###### Title: Toward robust malware detection and faithfully evaluating the robustness of neural networks Abstract: Adversarial examples have emerged as a profound challenge and a critical concern for several application domains, sparking interest in developing adversarially robust machine-learning (ML) models and reliable methods for assessing robustness. In this talk, I will discuss ... Read more
Ofir Lindenbaum Guest Lecture From BIU – Learning Club 19.6.22
The recording of Ofir's talk and slides is available here. ###### Meeting Info ###### Title: Deep learning for tabular biomedical data Abstract: Biomedical datasets are often low-sample-size or high-dimensional. Practitioners in this domain prefer linear or tree-based models over neural networks since the latter are harder to interpret and tend to overfit when applied to tabular ... Read more
BIU learning club – Jack Hessel – The Case for Reasoning Beyond Recognition
Location: Engineering building (1103), room 329 Title:The Case for Reasoning Beyond RecognitionAbstract:Algorithms that can jointly process modalities like images+text are needed for next generation search, accessibility, and robot interaction tools. Simply recognizing objects in images, however, is rarely sufficient; to truly be useful, machines must be capable of deeper commonsense inferences about sophisticated multimodal contexts. I'll ... Read more
BIU learning club – Sivan Sabato – Interactive Learning with Discriminative Feature Feedback
Location: Zoom meeting: https://us02web.zoom.us/j/4685913265Title:Interactive Learning with Discriminative Feature Feedback Abstract:In this talk I will discuss a model of learning with feature-based explanations, that we call Discriminative Feature Feedback. This model formalizes a natural notion of interactive learning with explanations. We study algorithms for this model, including robust algorithms for adversarial and stochastic settings, and derive insightful new results ... Read more
BIU learning club – Students’ talks
NameLab HeadTitle12:00-12:15Ori ErnstIdo DaganBeyond End-to-End: The Case of Multi Document Summarization12:15-12:30Shon OtmazginYoav GoldbergLingMess & F-COREF: Fast, Accurate, and Easy to Use models for Coreference Resolution12:30-12:45Lior Frenkel Jacob GCalibration of Medical Imaging Classification Systems with Weight Scaling12:45-13:00Coby PensoEthan FetayaFunctional Ensemble Distillation13:00-14:00Lunch
BIU learning club – Amir Globerson – Notions of simplicity in deep learning: From time series to images
Location:Engineering building (1103), room 329Title:Notions of simplicity in deep learning: From time series to images Abstract:It is standard practice indeep learning to train large models on relatively small datasets. This canpotentially lead to severe overfitting, but more often than not, test error isactually good. This phenomenon has prompted research on the so-called "ImplicitBias of Deep Learning Algorithms". ... Read more
BIU learning club – Tom Tirer – Exploring Deep Neural Collapse via Extended and Controlled Unconstrained Features Models
Location:Engineering building (1103), room 329Title:Exploring Deep Neural Collapse via Extended and Controlled Unconstrained Features ModelsAbstract:Training deep neural networks for classification often includes minimizing the training loss beyond the zero training error point. In this phase of training, a "neural collapse" (NC) behavior has been empirically observed: the variability of features (outputs of the penultimate layer) ... Read more
BIU learning club – Chaim Baskin – Graph Representation Learning Through Recoverability
Zoom link: https://us02web.zoom.us/j/4685913265Title:Graph Representation Learning Through Recoverability Abstract:Self-supervised learning methods became popular for graph representation learning because they do not rely on manual labels and offer better generalization. Contrastive methods based on mutual information maximization between augmented instances of the same object are widely used in self-supervised learning of representations. For graph-structured data, however, there are two ... Read more
BIU learning club – Itay Hubara – Toward Fast and Efficient Deep Learning
Zoom link: https://us02web.zoom.us/j/4685913265Title:Toward Fast and Efficient Deep Learning Abstract:Deep Neural Networks (DNNs) are now irreplaceable in various applications. However, DNNs require a vast amount of computational resources. In most cases, complex DNNs training requires several machines working in parallel (most commonly using data parallelism). Moreover, DNNs deployment on devices with limited computational power can be challenging and ... Read more
BIU learning club – Ravid Shwartz-Ziv – Exploring the Successes and Limitations of Deep Learning
Location:Engineering building (1103), room 329Title:Exploring the Successes and Limitations of Deep Learning Abstract:In this talk, we will explore the successes and limitations of deep learning networks and highlight the need for more rigorous evaluation. Tree ensemble models often outperform deep learning models for tabular data. However, recently there have been claims that some new deep-learning models ... Read more
Guest lecture – Vered Shwartz – Incorporating Commonsense Reasoning into NLP Models
Location:Engineering building (1103), room 329Title:Incorporating Commonsense Reasoning into NLP ModelsAbstract:Human language is often ambiguous, underspecified, and grounded in social norms. We employ commonsense knowledge and reasoning abilities to understand others. Endowing NLP models with the same abilities is imperative for reaching human-level language understanding and generation skills. In this talk, I will present several lines ... Read more
BIU learning club – Jonathan Berant – Retrieval in the age of large language models
Location:Engineering building (1103), room 329 Title:Retrieval in the age of large language modelsAbstract:Large language models have revolutionized natural language processing. However, such models have a limited receptive field and cannot be applied directly on tasks that require processing entire corpora. In this talk, I will talk about the role of information retrieval in the new landscape ... Read more
BIU learning club – Students’ talks
On Sunday 15.01.23, at 12:00 PM, we will have our second session of students’ presentations. In this session, four students from BIU will present their work. Note that, unlike regular learning club meetings, this meeting will last 2 hours, and will include lunch. It will take place at the Engineering building (1103) in room 329. ... Read more
BIU learning club – Dan Vilenchik – From theory to practice and back – Stance Detection as a case study
Location:Engineering building (1103), room 329Title:From theory to practice and back – Stance Detection as a case studyAbstract:Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this talk we describe a novel framework for stance detection. Our framework is ... Read more
BIU learning club – Dan Rosenbaum – Functa: data as neural fields
Location:Engineering building (1103), room 329Title:Functa: data as neural fieldsAbstract:It is common practice in deep learning to represent a measurement of the world on a discrete grid, e.g. a 2D grid of pixels. However, the underlying signal represented by these measurements is often continuous, e.g. the scene depicted in an image. A powerful continuous alternative is ... Read more
BIU learning club – Students’ talks
On Sunday 26.03.23, at 12:00 PM, we will have our third session of students’ presentations. In this session, four students from BIU will present their work. Note that, unlike regular learning club meetings, this meeting will last 2 hours, and will include lunch. It will take place at the Engineering building (1103) in room 329. ... Read more
BIU learning club – Alon Cohen – Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
Title:Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function ApproximationAbstract:We study learning tabular finite-horizon Markov Decision Processes with adversarially-chosen contexts. Following latest literature, we assume a realizable function class that maps between context and MDP as well as access to online regression oracles that fits the best function given prior observations. This setting ... Read more
BIU learning club – Yuval Pinter – When Language Models Meet Words
Location:Engineering building (1103), room 329Title:When Language Models Meet WordsAbstract:Over the last few years, deep neural models have taken over the field of natural language processing (NLP), brandishing great improvements on many of its sequence-level tasks. But the end-to-end nature of these models makes it hard to figure out whether the way they represent individual words ... Read more
BIU learning club – Assaf Arbelle – What is next in Vision and Language models?
Location:Building 1300 (students’ dorms), room 1Title:What is next in Vision and Language models?Abstract:In recent years, two mostly separated fields of machine learning, computer vision and natural language processing, have gradually become closer. Advancements in each field have greatly influenced the other, driven in part by the abundance of weekly annotated data in the form of ... Read more
BIU learning club – Amichai Painsky – Inferring the Unseen
Location:Engineering building (1103), room 329Title:Inferring the UnseenAbstract:Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved events is a basic problem in statistics and related fields, which was extensively studied in the context of point estimation. ... Read more
BIU learning club – Kfir Levy – Beyond SGD: Efficient Learning with Non i.i.d. Data
Location:Engineering building (1103), room 329Title:Beyond SGD: Efficient Learning with Non i.i.d. DataAbstract:The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods. The canonical algorithm for training learning models is SGD (Stochastic Gradient Descent), yet this method has several limitations. In particular, it relies on the assumption that data-points ... Read more
BIU learning club – Moshe Eliasof – Improving Graph Neural Networks with Learnable Propagation Operators
Location:Engineering building (1103), room 329Title:Improving Graph Neural Networks with Learnable Propagation OperatorsAbstract:Graph Neural Networks (GNNs) are limited in their propagation operators. In many cases, these operators often contain non-negative elements only and are shared across channels, limiting the expressiveness of GNNs. Moreover, some GNNs suffer from over-smoothing, limiting their depth. On the other hand, Convolutional ... Read more
BIU learning club – Shalev Shaer – Betting as a mechanism to make reliable discoveries
Zoom link: https://biu-ac-il.zoom.us/j/4685913265 Title:Betting as a mechanism to make reliable discoveries Abstract:This talk introduces a new statistical testing framework that allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dependency structure, and safely conclude whether a feature is conditionally associated with the response under study. We allow the processing of data points online, as soon ... Read more
BIU learning club – Yonatan Belinkov
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BIU learning club – Omri Abend
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BIU learning club – Students’ talks
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BIU learning club – Aryeh Kontorovich
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BIU AI and ML Learning Club – May 5, CANCELED
CS Bldg 503, Seminar Room 226UNFORTUNATELY THIS SESSION IS CANCELED We are Back with BIU AI & ML Learning Club ! On May 5, Hadar Averbuch-Elor from TAU will give a talk titled : Marrying Vision and Language: A Mutually Beneficial Relationship? Abstract: Foundation models that connect vision and language have recently shown great promise for a wide array of ... Read more
BIU AI and ML Learning Club – May 12
CS Bldg 503, Seminar Room 226On May 12, Louis Shekhtman from BIU will give a talk titled: Leveraging Big Data and Network Science to understand Philanthropy Abstract: While philanthropic support has increased in the past decade, there is limited quantitative knowledge about the patterns that characterize it and the mechanisms that drive its distribution. Here, we collected over 3 million ... Read more
BIU AI and ML Learning Club – May 19, Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
CS Bldg 503, Seminar Room 226On May 19, Guy Bar-Shalom from the Technion will give a talk titled: Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products Abstract: In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers. We propose an architecture that integrates both approaches, dubbed Subgraphormer, which ... Read more
BIU AI and ML Learning Club – May 26, Real-to-Sim: Towards interpretable and controllable digital twins (Note the Venue)
חדר ישיבות 329, הנדסהOn May 26, Dr. Or Litany from the Technion will give a talk titled: Real-to-Sim: Towards interpretable and controllable digital twins Abstract: Do we live in a simulation? Perhaps we should consider the possibility. Replicating real-world observations into a digital twin offers numerous potential benefits. For instance, in autonomous navigation, one could recreate safety-critical scenarios ... Read more
BIU AI and ML Learning Club – June 2, Do Stochastic, Feel Noiseless: Stable Optimization via a Double Momentum Mechanism
חדר ישיבות 329, הנדסהOn May 26, Dr. Kfir Levy from the Technion will give a talk titled: Do Stochastic, Feel Noiseless: Stable Optimization via a Double Momentum Mechanism Abstract: The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods, and the canonical algorithm for training learning models is SGD (Stochastic Gradient ... Read more
BIU AI and ML Learning Club – June 9, Testing for Dependency of Databases
CS Bldg 503, Seminar Room 226On June 9, Dr. Wasim Huleihel from the Tel Aviv university will give a talk titled: Testing for Dependency of Databases Abstract: In this talk, we investigate the problem of detecting the dependency between two random databases represented as matrices. This is formalized as a hypothesis testing problem, where under the null hypothesis, the two ... Read more
BIU AI and ML Learning Club – June 16, Revealing Latent Hierarchical Structures in High-Dimensional Data Using Hyperbolic Representations
חדר ישיבות 329, הנדסהOn June 16, Dr. Ronen Talmon from the Technion will give a talk titled: Revealing Latent Hierarchical Structures in High-Dimensional Data Using Hyperbolic Representations Abstract: The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods, and the canonical algorithm for training learning models is SGD (Stochastic Gradient Descent). ... Read more
BIU AI and ML Learning Club, June 23 – BIU Students research talks
CS Bldg 503, Seminar Room 226On June 23, we will have 4 BIU Students giving the following talks on their research progress. First hour (12:00-13:00) will be dedicated for the students talks Second hour (13:00 - 14:00) for networking. 12:00 - 12:15 Presenter: Osnat Drien Lab Head: Prof. Yael Amsterdamer Title: Query-Guided Resolution in Uncertain Databases Abstract: We present a ... Read more
BIU AI and ML Learning Club, June 30 – What Makes Data Suitable for Deep Learning?
CS Bldg 503, Seminar Room 226On June 30, Dr. Nadav Cohen from the Tel Aviv University will give a talk titled: What Makes Data Suitable for Deep Learning? Abstract: Deep learning is delivering unprecedented performance when applied to various data modalities, yet there are data distributions over which it utterly fails. The question of what makes a data distribution suitable ... Read more
BIU AI and ML Learning Club, July 7 – Local Glivenko-Cantelli (or: estimating the mean in infinite dimensions)
חדר ישיבות 329, הנדסהOn July 7, Prof. Aryeh Kontorovich from the Tel Aviv University will give a talk titled: Local Glivenko-Cantelli (or: estimating the mean in infinite dimensions) Abstract: If μ is a distribution over the d-dimensional Boolean cube {0,1}ᵈ, our goal is to estimate its mean p∈ᵈ based on n iid draws from μ. Specifically, we consider ... Read more
BIU AI and ML Learning Club, July 7 – Protecting AI From Theft with 2-Party Security
חדר ישיבות 329, הנדסהOn July 14, Dr. Adam Hakim from Microsoft WSSI will give a talk titled: Protecting AI From Theft with 2-Party Security Abstract: Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Moreover, the high ... Read more
BIU Learning Club, November 18 – Exploiting Symmetries for Learning in Deep Weight Spaces
חדר ישיבות 329, הנדסהOn November 18, Dr. Haggai Maron from the Technion will give a talk titled: Exploiting Symmetries for Learning in Deep Weight Spaces Abstract: This talk explores the emerging research direction that studies neural network weights as a novel data modality. We'll discuss recent advances in processing and analyzing raw weight matrices, which exhibit inherent symmetries ... Read more