Search Results for author:
Found 9 papers, 6 papers with code
Can Large Multimodal Models Uncover Deep Semantics Behind Images?
Understanding the deep semantics of images is essential in the era dominated by social media.
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference.
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Also, Bi-Drop needs only one mini-batch to estimate the sub-net so it achieves higher utility of training data.
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
Recently, Large Language Models (LLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge.
Enhancing Continual Relation Extraction via Classifier Decomposition
In this work, we point out that there exist two typical biases after training of this vanilla strategy: classifier bias and representation bias, which causes the previous knowledge that the model learned to be shaded.
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.
Abstractive Text Summarization
Grammatical Error Correction
+4
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding.
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.
Improved deep learning techniques in gravitational-wave data analysis
We point out that CNN models are robust to the variation of the parameter range of the GW waveform.
