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Found 9 papers, 4 papers with code
Accelerating Deep Learning with Millions of Classes
In our experiment, we demonstrate that the proposed frame-work is able to train deep learning models with millions of classes and achieve above 10×speedup compared to existing approaches.
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters.
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency.
How To Prevent the Continuous Damage of Noises To Model Training?
To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.
FaceMap: Towards Unsupervised Face Clustering via Map Equation
Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management.
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
The aggregate gradient strategy is a versatile module for mainstream CNN classifiers.
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation
There has been a recent surge of research interest in attacking the problem of social relation inference based on images.
Contextual Bandits with Random Projection
Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e. g., online advertisements.
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
In this paper, under a weaker assumption on noises, we study the problem of \underline{lin}ear stochastic {\underline b}andits with h{\underline e}avy-{\underline t}ailed payoffs (LinBET), where the distributions have finite moments of order $1+\epsilon$, for some $\epsilon\in (0, 1]$.
