Search Results for author:
Found 14 papers, 2 papers with code
Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow
Furthermore, we propose an entropy-based noise-controlling strategy to enable the injected noise to be adaptively constrained regarding the smoothness of the similarity distribution.
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks
Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level factors.
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction Attacks
Guided by the model, we further introduce: (1) a similarity-based training-free watermarking method for plug-and-play and flexible watermarking, and (2) a distribution-based multi-step watermark information transmission strategy for robust watermarking.
A Survey on Federated Learning in Human Sensing
However, its reliance on detailed and often privacy-sensitive data as the basis for its machine learning (ML) models raises significant legal and ethical concerns.
WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding
Speech large language models (speech-LLMs) integrate speech and text-based foundation models to provide a unified framework for handling a wide range of downstream tasks.
Prompting Whisper for QA-driven Zero-shot End-to-end Spoken Language Understanding
The system is efficiently trained with prefix-tuning, optimising a minimal set of parameters rather than the entire Whisper model.
DiaLoc: An Iterative Approach to Embodied Dialog Localization
However, most existing works in embodied dialog research focus on navigation and leave the localization task understudied.
Economic Forces in Stock Returns
Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors.
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition
In previous works, latency was optimised by truncating the online attention weights based on the hard alignments obtained from conventional ASR models, without taking into account the potential loss of ASR accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding
This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer
The multiple-hypothesis approach yields a relative reduction of 3. 3% WER on the CHiME-4's single-channel real noisy evaluation set when compared with the single-hypothesis approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transformer-based Streaming ASR with Cumulative Attention
In this paper, we propose an online attention mechanism, known as cumulative attention (CA), for streaming Transformer-based automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Head-synchronous Decoding for Transformer-based Streaming ASR
Online Transformer-based automatic speech recognition (ASR) systems have been extensively studied due to the increasing demand for streaming applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end Speech Recognition with Adaptive Computation Steps
In this paper, we present Adaptive Computation Steps (ACS) algo-rithm, which enables end-to-end speech recognition models to dy-namically decide how many frames should be processed to predict a linguistic output.
Ranked #18 on
Speech Recognition
on AISHELL-1
