Search Results for author:
Found 10 papers, 9 papers with code
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Ranked #3 on
Image Generation
on CAT 256x256
GANSynth: Adversarial Neural Audio Synthesis
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
In Search of Lost Domain Generalization
As a first step, we realize that model selection is non-trivial for domain generalization tasks.
Ranked #42 on
Domain Generalization
on PACS
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003.
Diffusion-LM Improves Controllable Text Generation
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #66 on
Sentiment Analysis
on SST-2 Binary classification
PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning.
Towards GAN Benchmarks Which Require Generalization
For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.
