Learning Club

On Sunday 21.11.2021 at 12:00, we will host Yossi Adi, from FAIR. Zoom: https://us02web.zoom.us/j/85941444168?pwd=S3pvNnVYTkx6RWsvSzFxTVVvdlFlZz09 Meeting ID: 859 4144 4168 Passcode: 584021 Title A Textless Approach for Generative Spoken Language Modeling Abstract An open question for AI research is creating systems that learn from natural interactions as infants learn their first language(s): spontaneously and without access ... Read more

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

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

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 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

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 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

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

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

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

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

###### 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

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

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

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

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

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

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

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

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

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

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

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UNFORTUNATELY 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