A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified view of contrastive and supervised learning improves model performance.
Memory split advantage: thinner networks outperform a single wide network.
Memory is increasingly often the bottleneck when training neural network models. Despite this, techniques to lower the overall memory requirements of training have been less widely studied compared to the extensive literature on reducing the memory requirements of inference. In this paper we study a fundamental questio…
Adversarial training has been shown to regularize deep neural networks in addition to increasing their robustness to adversarial examples. However, its impact on very deep state of the art networks has not been fully investigated. In this paper, we present an efficient approach to perform adversarial training by pertur…
This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways? We develop novel orthogonality regularizations on training deep CNNs, utilizing various adva…
In many learning situations, resources at inference time are significantly more constrained than resources at training time. This paper studies a general paradigm, called Differentiable ARchitecture Compression (DARC), that combines model compression and architecture search to learn models that are resource-efficient a…
Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence. In this work, we propose a novel CNN training algorithm Sparse Weight Activation Training (SWAT). SWAT …
Active testing reduces label costs for efficient model evaluation.
RS-NN predicts belief functions for classification, improving accuracy and uncertainty estimation.
Skip connections make adversarial examples more transferable in ResNets.
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task. To make matters worse, there is an intrinsic bias in these adversarial algorithms. Here, we organise the problems faced: a) Model Dependence…
TempBalance improves neural network training by balancing learning rates across layers.
A new OOD detection method OTOD uses optimal transport theory to improve model performance.
Proposes RBGP framework for efficient block sparse neural networks.
A new training method speeds up ResNet training by 3x with minimal accuracy loss.
New insights into learning rates and batch sizes for neural networks using random matrix theory.
MIMONets speed up neural network inference by processing multiple inputs in parallel.