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Found 7 papers, 2 papers with code
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded, resulting in high latency and significant wastes of the parallel processing power of modern accelerators.
Unified Matrix Factorization with Dynamic Multi-view Clustering
In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations.
Contrastive Knowledge Amalgamation for Unsupervised Image Classification
Current methods focus on coarsely aligning teachers and students in the common representation space, making it difficult for the student to learn the proper decision boundaries from a set of heterogeneous teachers.
Feature Map Testing for Deep Neural Networks
Current test metrics, however, are primarily concerned with the neurons, which means that test cases that are discovered either by guided fuzzing or selection with these metrics focus on detecting fault-inducing neurons while failing to detect fault-inducing feature maps.
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing
To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets.
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
In this paper, we propose the first fully push-forward-based Distributional Reinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER).
Neural Architecture Search via Ensemble-based Knowledge Distillation
Neural Architecture Search (NAS) automatically searches for well-performed network architectures from a given search space.
