# ICLR 2023 firstbacksecondback Search All 2023 Events #### 282 Results Poster Wed 2:30 REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH Duc Hoang ⋅ Shiwei Liu ⋅ Radu Marculescu ⋅ Zhangyang Wang Oral Wed 2:00 REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH Duc Hoang ⋅ Shiwei Liu ⋅ Radu Marculescu ⋅ Zhangyang Wang Poster Pruning Deep Neural Networks from a Sparsity Perspective Enmao Diao ⋅ Ganghua Wang ⋅ Jiawei Zhang ⋅ Yuhong Yang ⋅ Jie

NeurIPS Proceedings Search Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees Jue WANG, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track Abstract Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of

To learn an unbounded problem is to generalize well from a limited set of training data. In humans, robust language acquisition requires language learners to form strong generalizations on the basis of very limited evidence (Chomsky 1980). These generalizations seem to require the acquisition of functional abstractions of some sort. Various analysis of these abstractions have been put forth in the context of replicating this generalization in artificial settings (Fodor & Pylyshyn 1988, G. F. Marcus 1998a, L

###########input#################### def ResNet50(input_shape = (64, 64, 3), classes = 6): … ####output################################### Test failed Expected value [‘Add’, (None, 15, 15, 256), 0] does not match …

Python, PRML, machine learning

Learn how different dimensions are used in convolutional neural networks

Google's Neural Machine Translation looks complex from a distance - but not if you build it up piece by piece

Toggle navigation Alianna J. Maren Alianna J. Maren Statistical Mechanics, Neural Networks, Artificial Intelligence Browsed by Category: A Resource – THE BOOK Statistical Mechanics, Neural Networks, and Machine Learning Directed vs. Undirected Graphs in NNs: The (Surprising!) Implications Directed vs. Undirected Graphs in NNs: The (Surprising!) Implications June 20, 2019 AJMaren Comments 0 Comment Most of us don’t always use graph language to describe neural networks, but if we dig into the implications

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold

I train neural networks, which are a type of machine learning program that imitates the way human brains learn. (Technically, they’re artificial neural networks, to differentiate them from the biological sort). Unlike traditional computer programming, where a human programmer comes up with a long set of rules for a computer to follow, with neural networks, the computer learns by example and comes up with its own rules