Search Results for author:
Found 14 papers, 9 papers with code
Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers
Non-linear functions are prevalent in Transformers and their lightweight variants, incurring substantial and frequently underestimated hardware costs.
CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
We also developed a training mechanism, MocoRank, to handle the intra-class variation, the ordinal relationship between different classes, and the data imbalance problem.
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning
In this work, we propose CoT-Influx, a novel approach that pushes the boundary of few-shot Chain-of-Thoughts (CoT) learning to improve LLM mathematical reasoning.
Ranked #105 on
Arithmetic Reasoning
on GSM8K
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Our method, for the first time, can quantize both weights and activations in the LLaMA-13B to only 4-bit and achieves an average score of 63. 1 on the common sense zero-shot reasoning tasks, which is only 5. 8 lower than the full-precision model, significantly outperforming the previous state-of-the-art by 12. 7 points.
VideoPro: A Visual Analytics Approach for Interactive Video Programming
Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection.
Variation-aware Vision Transformer Quantization
We also find that the variations in ViTs cause training oscillations, bringing instability during quantization-aware training (QAT).
Efficient Quantization-aware Training with Adaptive Coreset Selection
Compared with previous coreset selection methods, our method significantly improves QAT performance with different dataset fractions.
SDQ: Stochastic Differentiable Quantization with Mixed Precision
In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used.
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels.
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
Human-Object Interaction (HOI) detection is an important problem to understand how humans interact with objects.
Ranked #28 on
Human-Object Interaction Detection
on V-COCO
PaStaNet: Toward Human Activity Knowledge Engine
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on
Human-Object Interaction Detection
on HICO
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations
NeuronInspect first identifies the existence of backdoor attack targets by generating the explanation heatmap of the output layer.
HAKE: Human Activity Knowledge Engine
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on
Human-Object Interaction Detection
on HICO
(using extra training data)
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results.
Ranked #29 on
Human-Object Interaction Detection
on V-COCO
