6 citations · 11 across the 11 of their papers we have counts for
11 papers
Alethia: A Foundational Encoder for Voice Deepfakes
Yi Zhu, Brahmi Dwivedi, Jayaram Raghuram +1
Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, downstream finetuning has now rea…
Inverting Trojans in LLMs
Zhengxing Li, Guangmingmei Yang, Jayaram Raghuram +2
While effective backdoor detection and inversion schemes have been developed for AIs used e.g. for images, there are challenges in "porting" these methods to LLMs. First, the LLM i…
Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
Jihye Choi, Jayaram Raghuram, Yixuan Li +1
Advancements in foundation models (FMs) have led to a paradigm shift in machine learning. The rich, expressive feature representations from these pre-trained, large-scale FMs are l…
PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms
Yilong Li, Jingyu Liu, Hao Zhang +7
Deploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy c…
A Study of Backdoors in Instruction Fine-tuned Language Models
Jayaram Raghuram, George Kesidis, David J. Miller
Backdoor data poisoning, inserted within instruction examples used to fine-tune a foundation Large Language Model (LLM) for downstream tasks (\textit{e.g.,} sentiment prediction),…
Stratified Adversarial Robustness with Rejection
Jiefeng Chen, Jayaram Raghuram, Jihye Choi +3
Recently, there is an emerging interest in adversarially training a classifier with a rejection option (also known as a selective classifier) for boosting adversarial robustness. W…
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li +4
Pre-training representations (a.k.a. foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled d…
Concept-based Explanations for Out-Of-Distribution Detectors
Jihye Choi, Jayaram Raghuram, Ryan Feng +3
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers. While a myriad of methods have focused on improvi…
Few-Shot Domain Adaptation For End-to-End Communication
Jayaram Raghuram, Yijing Zeng, Dolores García Martí +4
The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently be…
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha +1
Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD) inputs, is critical for classifiers (including deep neural networks or DNNs) deployed in real-world ap…
Generation bidding game with flexible demand
Yuquan Shan, Jayaram Raghuram, George Kesidis +5
For a simple model of price-responsive demand, we consider a deregulated electricity marketplace wherein the grid (ISO, retailer-distributor) accepts bids per-unit supply from gene…