Search Results for author:
Found 13 papers, 5 papers with code
Banishing LLM Hallucinations Requires Rethinking Generalization
We use our findings to design a first generation model for removing hallucinations -- Lamini-1 -- that stores facts in a massive mixture of millions of memory experts that are retrieved dynamically.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
Empirically Characterizing Overparameterization Impact on Convergence
A long-held conventional wisdom states that larger models train more slowly when using gradient descent.
Coloring Big Graphs with AlphaGoZero
We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications.
Language Modeling at Scale
We show how Zipf's Law can be used to scale up language modeling (LM) to take advantage of more training data and more GPUs.
A Proposed Hierarchy of Deep Learning Tasks
As the pace of deep learning innovation accelerates, it becomes increasingly important to organize the space of problems by relative difficultly.
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks
We propose the multi-head convolutional neural network (MCNN) architecture for waveform synthesis from spectrograms.
Deep Learning Scaling is Predictable, Empirically
As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art.
Block-Sparse Recurrent Neural Networks
Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms.
Mixed Precision Training
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1.
Exploring Sparsity in Recurrent Neural Networks
Benchmarks show that using our technique model size can be reduced by 90% and speed-up is around 2x to 7x.
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
