20 citations · 41 across the 22 of their papers we have counts for
22 papers
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
Zhaorun Chen, Zhuokai Zhao, Zhihong Zhu +4
Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback…
Evaluating and Improving Continual Learning in Spoken Language Understanding
Muqiao Yang, Xiang Li, Umberto Cappellazzo +2
Continual learning has emerged as an increasingly important challenge across various tasks, including Spoken Language Understanding (SLU). In SLU, its objective is to effectively h…
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Sim…
Domain Adaptation for Contrastive Audio-Language Models
Soham Deshmukh, Rita Singh, Bhiksha Raj
Audio-Language Models (ALM) aim to be general-purpose audio models by providing zero-shot capabilities at test time. The zero-shot performance of ALM improves by using suitable tex…
A General Framework for Learning from Weak Supervision
Hao Chen, Jindong Wang, Lei Feng +6
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algor…
On Catastrophic Inheritance of Large Foundation Models
Hao Chen, Bhiksha Raj, Xing Xie +1
Large foundation models (LFMs) are claiming incredible performances. Yet great concerns have been raised about their mythic and uninterpreted potentials not only in machine learnin…
PAM: Prompting Audio-Language Models for Audio Quality Assessment
Soham Deshmukh, Dareen Alharthi, Benjamin Elizalde +5
While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Mod…
AugSumm: towards generalizable speech summarization using synthetic labels from large language model
Jee-weon Jung, Roshan Sharma, William Chen +2
Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. Given variations in information captured and phrasing, recordings can be summarized in mu…
Token Prediction as Implicit Classification to Identify LLM-Generated Text
Yutian Chen, Hao Kang, Vivian Zhai +3
This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation. Instead of adding an additional classification layer t…
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
Thanh-Dat Truong, Utsav Prabhu, Bhiksha Raj +2
Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies…
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
Muhammad Ahmed Shah, Roshan Sharma, Hira Dhamyal +10
It has been shown that Large Language Model (LLM) alignments can be circumvented by appending specially crafted attack suffixes with harmful queries to elicit harmful responses. To…
Pairwise Similarity Learning is SimPLE
Yandong Wen, Weiyang Liu, Yao Feng +5
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face…
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models
Muqiao Yang, Chunlei Zhang, Yong Xu +4
Speech enhancement aims to improve the quality of speech signals in terms of quality and intelligibility, and speech editing refers to the process of editing the speech according t…
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms
Joseph Konan, Shikhar Agnihotri, Ojas Bhargave +4
Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted i…
Continual Contrastive Spoken Language Understanding
Umberto Cappellazzo, Enrico Fini, Muqiao Yang +3
Recently, neural networks have shown impressive progress across diverse fields, with speech processing being no exception. However, recent breakthroughs in this area require extens…
Prompting Audios Using Acoustic Properties For Emotion Representation
Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh +3
Emotions lie on a continuum, but current models treat emotions as a finite valued discrete variable. This representation does not capture the diversity in the expression of emotion…
Completing Visual Objects via Bridging Generation and Segmentation
Xiang Li, Yinpeng Chen, Chung-Ching Lin +6
This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components. Our method, named MaskCo…
Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech
Dareen Alharthi, Roshan Sharma, Hira Dhamyal +3
Modern speech synthesis systems have improved significantly, with synthetic speech being indistinguishable from real speech. However, efficient and holistic evaluation of synthetic…
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
Xiang Li, Jinglu Wang, Xiaohao Xu +4
Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and back…
Importance of negative sampling in weak label learning
Ankit Shah, Fuyu Tang, Zelin Ye +2
Weak-label learning is a challenging task that requires learning from data "bags" containing positive and negative instances, but only the bag labels are known. The pool of negativ…
Training Audio Captioning Models without Audio
Soham Deshmukh, Benjamin Elizalde, Dimitra Emmanouilidou +3
Automated Audio Captioning (AAC) is the task of generating natural language descriptions given an audio stream. A typical AAC system requires manually curated training data of audi…
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Hao Chen, Jindong Wang, Ankit Shah +5
Pre-training on large-scale datasets and then fine-tuning on downstream tasks have become a standard practice in deep learning. However, pre-training data often contain label noise…