Search Results for author:
Found 10 papers, 0 papers with code
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Deep learning-based recommendation systems (e. g., DLRMs) are widely used AI models to provide high-quality personalized recommendations.
RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments
Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership.
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience.
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring
Our methodology efficiently discovers an interpretable and generalizable zero-cost proxy that gives state of the art score-accuracy correlation on all datasets and search spaces of NASBench-201 and Network Design Spaces (NDS).
Mitigating Sampling Bias and Improving Robustness in Active Learning
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Robust Contrastive Active Learning with Feature-guided Query Strategies
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.
RHNAS: Realizable Hardware and Neural Architecture Search
The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges.
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease
We model Alzheimer’s disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge.
ML-driven Malware that Targets AV Safety
Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption.
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.
