Search Results for author:
Found 9 papers, 4 papers with code
CodeExp: Explanatory Code Document Generation
Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones.
TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches
TACCL uses a novel communication sketch abstraction to get crucial information from the designer to significantly reduce the search space and guide the synthesizer towards better algorithms.
Program Merge Conflict Resolution via Neural Transformers
Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools.
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
Scaling Distributed Training with Adaptive Summation
This paper introduces a novel method to combine gradients called Adasum (for adaptive sum) that converges faster than prior work.
Distributed Training of Embeddings using Graph Analytics
This paper presents a distributed training framework for a class of applications that use Skip-gram-like models to generate embeddings.
CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs
Just as the hardware ISA interface enabled hardware advances to proceed independent of software advances in the compiler and language runtimes, HISA decouples compiler optimizations and runtimes for supporting FHE applications from advancements in the underlying FHE schemes.
High Five: Improving Gesture Recognition by Embracing Uncertainty
We introduce a new statistical quantization approach that mitigates these problems by (1) during training, producing gesture-specific codebooks, HMMs, and error models for gesture sequences; and (2) during classification, exploiting the error model to explore multiple feasible HMM state sequences.
Parallel Stochastic Gradient Descent with Sound Combiners
This paper proposes SYMSGD, a parallel SGD algorithm that, to a first-order approximation, retains the sequential semantics of SGD.
