Calendar of Events
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2 events,
Continuous vs. Discrete Optimization of Deep Neural Networks. Nadav Cohen (TAU).
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
Learning to Cooperate and Compete in Diplomacy. Dr. Noam Brown, FACEBOOK. (CS Colloquium)
Learning to Cooperate and Compete in Diplomacy. Dr. Noam Brown, FACEBOOK. (CS Colloquium)
Dr. Noam Brown Will lecture on Learning to Cooperate and Compete in Diplomacy AI has made incredible progress in purely adversarial games such as chess, go, and poker. However, the real world involves a complex mixture of cooperation and competition, sometimes with irrational or suboptimal participants, and in these settings past AI techniques fall apart. For ... Read more
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Deep Learning for Representation Learning by Dr. Uri Shaham Yale University (CS Colloquium)
Deep Learning for Representation Learning by Dr. Uri Shaham Yale University (CS Colloquium)
Dr. Uri Shaham. Yale University Will lecture on Deep Learning for Representation Learning In this talk I will present two deep learning-based algorithms for representation learning. In the first half of the talk I will present SpectralNet, a deep learning approach for spectral clustering, which is scalable and allows for straight-forward out of sample extension. ... Read more
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Learning to Plan in the Real World – Roni Stern, Ben Gurion University
Learning to Plan in the Real World – Roni Stern, Ben Gurion University
Prof. Roni Stern, Ben Gurion University Learning to Plan in the Real World Planning is often referred to as the art of thinking before acting. As such, automated planning is a long-term goal of Artificial Intelligence (AI). Most planning algorithms in the literature are model-based, in the sense that they assume a compositional model of ... Read more
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StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators. Rinon Gal (TAU and NVIDIA).
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
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Robust Android Malware Detection, Harel Berger (Ariel U.)
Robust Android Malware Detection, Harel Berger (Ariel U.)
The lecture will be given by Mr. Harel Berger from Ariel University in zoom.. zoom link: https://us02web.zoom.us/j/83383478356 Title: Robust Android Malware Detection Abstract: A growing number of malware detection methods are heavily based on Machine Learning (ML) and Deep Learning techniques. However, these classifiers are often vulnerable to evasion attacks, in which an adversary manipulates a malicious instance from being detected. This ... Read more
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Solving Stochastic Programming Problems by Operator Splitting. Jonathan Eckstein, Rutgers Business School.
Solving Stochastic Programming Problems by Operator Splitting. Jonathan Eckstein, Rutgers Business School.
Solving Stochastic Programming Problems by Operator Splitting A B S T R A C T This talk describes the solution of convex optimization problems that include uncertainty modeled by a finite but potentially very large multi-stage scenario tree. In 1991, Rockafellar and Wets proposed the progressive hedging (PH) algorithm to solve such problems. This method ... Read more
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1 event,
Model-Based Deep Learning in Signal Processing and Communications, Dr. Nir Shlezinger (BGU)
Model-Based Deep Learning in Signal Processing and Communications, Dr. Nir Shlezinger (BGU)
Title: Model-Based Deep Learning in Signal Processing and Communications Abstract: Recent years have witnessed a dramatically growing interest in machine learning (ML) methods. These data-driven trainable structures have demonstrated an unprecedented empirical success in various applications, including computer vision and speech processing. The benefits of ML-driven techniques over traditional model-based approaches are twofold: First, ML ... Read more