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Found 9 papers, 1 papers with code
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation
Denoising Diffusion models have demonstrated their proficiency for generative sampling.
BERT Learns (and Teaches) Chemistry
Modern computational organic chemistry is becoming increasingly data-driven.
Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks
The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks.
Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity
Kernel sparsity ("dying ReLUs") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity.
Understanding Adversarial Robustness Through Loss Landscape Geometries
In this paper, we harness the state-of-the-art "filter normalization" technique of loss-surface visualization to qualitatively understand the consequences of using adversarial training data augmentation as the explicit regularization technique of choice.
Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance
Attribute prior avoidance entails subconscious or willful non-modeling of (meta)attributes that datasets are oft born with, such as the 40 semantic facial attributes associated with the CelebA and CelebA-HQ datasets.
Grassmannian initialization: Neural network initialization using sub-space packing
We recently observed that convolutional filters initialized farthest apart from each other using offthe- shelf pre-computed Grassmannian subspace packing codebooks performed surprisingly well across many datasets.
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.
A Seed-Augment-Train Framework for Universal Digit Classification
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.
