Search Results for author:
Found 14 papers, 4 papers with code
Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models
However, one of the key limitations that have been observed with these models is their propensity to hallucinate significantly more often than larger models.
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler
In this project, we introduce the first RL environment for the MLIR compiler, dedicated to facilitating MLIR compiler research, and enabling automatic code optimization using Multi-Action Reinforcement Learning.
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Two notable techniques employed to achieve this goal are Hardware-aware Neural Architecture Search (HW-NAS) and Automatic Code Optimization (ACO).
Hardware Aware Neural Architecture Search
Neural Architecture Search
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Curriculum Learning for Small Code Language Models
In this paper, we explore the potential of curriculum learning in enhancing the performance of these models.
Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks.
LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers
In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs.
Automatic Generation of Python Programs Using Context-Free Grammars
In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems.
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations
In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers.
A Deep Learning Based Cost Model for Automatic Code Optimization
Unlike previous models, the proposed one works on full programs and does not rely on any heavy feature engineering.
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
Machine learning (ML) models are widely used in many important domains.
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compilers (sparse neural networks here stand for networks that can be accelerated with sparse tensor algebra techniques).
GraphIt - A High-Performance DSL for Graph Analytics
This paper introduces GraphIt, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures.
Programming Languages
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines.
