2 citations · 2 across the 28 of their papers we have counts for
10 papers · 1 filter
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
Quanling Zhao, Jiaying Yang, Ye Tian +5
Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are…
A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models
Jason Kong, Nilesh Prasad Pandey, Flavio Ponzina +1
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architect…
INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing +1
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many real-world systems, where acc…
QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
Nilesh Prasad Pandey, Jangseon Park, Onat Gungor +2
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantiz…
FaTRQ: Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems
Tianqi Zhang, Flavio Ponzina, Tajana Rosing
Approximate Nearest-Neighbor Search (ANNS) is a key technique in retrieval-augmented generation (RAG), enabling rapid identification of the most relevant high-dimensional embedding…
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
Nilesh Prasad Pandey, Shriniwas Kulkarni, David Wang +3
Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, current HDC-based applications often…
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
Harsh Vardhan, Xiaofan Yu, Tajana Rosing +1
Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a…
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
Cagla Ipek Kocal, Onat Gungor, Aaron Tartz +2
Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adv…
TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress
Onat Gungor, Amanda Sofie Rios, Nilesh Ahuja +1
Detecting out-of-distribution (OOD) data is a fundamental challenge in the deployment of machine learning models. From a security standpoint, this is particularly important because…
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs
Lanxiang Hu, Tajana Rosing, Hao Zhang
Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance while meeting latency and privacy constraints. Howev…