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03/06/2023
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
Transfer learning and ensembling are two popular techniques for improvin...
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09/08/2022
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
A fundamental property of deep learning normalization techniques, such a...
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12/29/2021
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
Channel decoding, channel detection, channel assessment, and resource ma...
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07/21/2021
On the Memorization Properties of Contrastive Learning
Memorization studies of deep neural networks (DNNs) help to understand w...
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06/29/2021
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Despite the conventional wisdom that using batch normalization with weig...
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07/16/2020
On Power Laws in Deep Ensembles
Ensembles of deep neural networks are known to achieve state-of-the-art ...
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05/14/2020
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
One of the generally accepted views of modern deep learning is that incr...
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11/13/2019
Structured Sparsification of Gated Recurrent Neural Networks
Recently, a lot of techniques were developed to sparsify the weights of ...
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12/12/2018
Bayesian Sparsification of Gated Recurrent Neural Networks
Bayesian methods have been successfully applied to sparsify weights of n...
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10/25/2018
Bayesian Compression for Natural Language Processing
In natural language processing, a lot of the tasks are successfully solv...
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04/10/2018
Monotonic models for real-time dynamic malware detection
In dynamic malware analysis, programs are classified as malware or benig...
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04/10/2018
Semantic embeddings for program behavior patterns
In this paper, we propose a new feature extraction technique for program...
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07/31/2017