Search Results for author:
Found 7 papers, 1 papers with code
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models.
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.
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).
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking
Neural networks are becoming increasingly ubiquitous in a wide range of use cases.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
