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Found 16 papers, 4 papers with code
The performance of orthogonal multi-matching pursuit under RIP
In particular, for $M=s^a$ with $a\in [0, 1/2]$, OMMP(M) can recover slowly-decaying $s$-sparse signal within $O(s^{1-a})$ iterations.
Stochastic Variance Reduced Riemannian Eigensolver
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations.
Towards Practical Alternating Least-Squares for CCA
To promote the practical use of ALS for CCA, we propose truly alternating least-squares.
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
Weight decay is a simple yet powerful regularization technique that has been very widely used in training of deep neural networks (DNNs).
Towards Better Generalization of Adaptive Gradient Methods
Adaptive gradient methods such as AdaGrad, RMSprop and Adam have been optimizers of choice for deep learning due to their fast training speed.
Fairness-aware Federated Learning
Federated Learning is a machine learning technique where a network of clients collaborates with a server to learn a centralized model while keeping data localized.
A Comprehensively Tight Analysis of Gradient Descent for PCA
We further give the first worst-case analysis that achieves a rate of convergence at $O(\frac{1}{\epsilon}\log\frac{1}{\epsilon})$.
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
Specifically, we consider the generation of cross-domain videos from two sets of latent factors, one encoding the static information and another encoding the dynamic information.
No existence of linear algorithm for Fourier phase retrieval
Fourier phase retrieval, which seeks to reconstruct a signal from its Fourier magnitude, is of fundamental importance in fields of engineering and science.
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation
A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL.
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Remarkably, by incorporating conditional information from the powerful CLIP model, our method can boost the current SOTA accuracy by 10-20 absolute points in many cases.
Ranked #1 on
Image Classification
on Food-101N
(using extra training data)
A Cover Time Study of a non-Markovian Algorithm
In this work, we stand on a theoretical perspective and show that the negative feedback strategy (a count-based exploration method) is better than the naive random walk search.
SR-R$^2$KAC: Improving Single Image Defocus Deblurring
To further alleviate the contingent effect of recursive stacking, i. e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions.
Behavior Optimized Image Generation
We show that BoigLLM outperforms 13x larger models such as GPT-3. 5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
