Search Results for author: Ted Willke

Found 10 papers, 6 papers with code

LeanVec: Search your vectors faster by making them fit

1 code implementation26 Dec 2023 Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, Mark Hildebrand, Ted Willke

LeanVec-OOD uses a novel technique for dimensionality reduction that considers the query and database distributions to simultaneously boost the accuracy and the performance of the framework even further (even presenting competitive results when the query and database distributions match).

Cross-Modal Retrieval Dimensionality Reduction +1

Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks

2 code implementations20 Dec 2023 Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks.

Code Generation

Scope is all you need: Transforming LLMs for HPC Code

2 code implementations18 Aug 2023 Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks.

Code Completion

Similarity search in the blink of an eye with compressed indices

1 code implementation7 Apr 2023 Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, Ted Willke

In this work, we present new techniques and systems for creating faster and smaller graph-based indices.

Quantization

Toward a Geometrical Understanding of Self-supervised Contrastive Learning

no code implementations13 May 2022 Romain Cosentino, Anirvan Sengupta, Salman Avestimehr, Mahdi Soltanolkotabi, Antonio Ortega, Ted Willke, Mariano Tepper

When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.

Contrastive Learning Data Augmentation +2

Procrustean Orthogonal Sparse Hashing

no code implementations8 Jun 2020 Mariano Tepper, Dipanjan Sengupta, Ted Willke

We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.

NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning

1 code implementation20 Sep 2019 Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Sophia Shao, Krste Asanovic, Ion Stoica

However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions.

Distributed, Parallel, and Cluster Computing Performance Programming Languages

A View on Deep Reinforcement Learning in System Optimization

no code implementations4 Aug 2019 Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Joseph Gonzalez, Krste Asanovic, Ion Stoica

We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization.

reinforcement-learning Reinforcement Learning (RL)

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