Deep Learning
Benchmark_Sample_Efficiency_neurips_data
Despite the exciting progress in the field and the abundance of new methods proposed, how these algorithms compare against each other remains unclear.
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
Second-order information, in the form of Hessian-or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems.
Referring Transformer: A One-step Approach to Multi-task Visual Grounding Muchen Li1,2 Leonid Sigal
Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures.
Invariant and Transportable Representations for Anti-Causal Domain Shifts
However, many real-world problems are "anti-causal" in the sense that
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Multivariate time-series forecasting plays a crucial role in many real-world applications.
Neural Manifold Ordinary Differential Equations
In this paper, we study normalizing flows on manifolds.
