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Found 23 papers, 12 papers with code
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Large pre-trained language models perform remarkably well on tasks that can be done "in one pass", such as generating realistic text or synthesizing computer programs.
Program Synthesis with Large Language Models
Our largest models, even without finetuning on a code dataset, can synthesize solutions to 59. 6 percent of the problems from MBPP using few-shot learning with a well-designed prompt.
SMYRF - Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate attention.
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
SMYRF: Efficient Attention using Asymmetric Clustering
We also show that SMYRF can be used interchangeably with dense attention before and after training.
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Program synthesis is challenging largely because of the difficulty of search in a large space of programs.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.
Learning to Represent Programs with Property Signatures
We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms.
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality.
Ranked #18 on
Conditional Image Generation
on ImageNet 128x128
Small-GAN: Speeding Up GAN Training Using Core-sets
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes.
Consistency Regularization for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort.
Ranked #5 on
Conditional Image Generation
on ArtBench-10 (32x32)
Improving Differentially Private Models with Active Learning
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records.
Discriminator Rejection Sampling
We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution.
Skill Rating for Generative Models
We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players.
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models.
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
Ranked #19 on
Conditional Image Generation
on ImageNet 128x128
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications.
Is Generator Conditioning Causally Related to GAN Performance?
Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs).
Changing Model Behavior at Test-Time Using Reinforcement Learning
Machine learning models are often used at test-time subject to constraints and trade-offs not present at training-time.
Conditional Image Synthesis With Auxiliary Classifier GANs
We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.
Ranked #13 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
Semi-Supervised Learning with Generative Adversarial Networks
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels.
Faster Asynchronous SGD
Approaches have been proposed to circumvent this problem that quantify staleness in terms of the number of elapsed updates.
