Genre
Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark Brianna M. Karpowicz 1,2 Joel Y e
However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner.
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging.
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Convolutional Neural Networks (CNNs) (LeCun et al., 1989) have become a widely used and powerful tool for computer vision tasks, in large part due to their ability to achieve translation
Set-based Neural Network Encoding Without Weight Tying Bruno Andreis
Furthermore, our S et-based N eural network E ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks.
Testing Semantic Importance via Betting
Providing guarantees on the decision-making processes of autonomous systems, often based on complex black-box machine learning models, is paramount for their safe deployment. This need motivates efforts towards responsible artificial intelligence, which broadly entails questions of reliability, robustness, fairness, and interpretability.
Tree-Based Diffusion Schrรถdinger Bridge with Applications to Wasserstein Barycenters
While OT commonly seeks at computing the transport plan that minimizes the cost of moving between two distributions, it can naturally be extended to the multi-marginal setting (mOT) when considering several distributions.
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Surprisingly, we find that most of the studied methods yield similar results.