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Found 21 papers, 3 papers with code
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.
SPE: Symmetrical Prompt Enhancement for Fact Probing
In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.
Foundation Models for Semantic Novelty in Reinforcement Learning
Effectively exploring the environment is a key challenge in reinforcement learning (RL).
Guided Conditional Diffusion for Controllable Traffic Simulation
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
Robust and Controllable Object-Centric Learning through Energy-based Models
Humans are remarkably good at understanding and reasoning about complex visual scenes.
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Autoregressive generative models are commonly used, especially for those tasks involving sequential data.
Sparse Mixture-of-Experts are Domain Generalizable Learners
It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-of-the-art (SOTA) DG algorithms on multiple DG datasets.
Ranked #9 on
Domain Generalization
on DomainNet
(using extra training data)
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations
One of the greatest challenges of reinforcement learning is efficient exploration, especially when training signals are sparse or deceptive.
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems.
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision.
Rethinking Distributional Matching Based Domain Adaptation
In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods.
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score.
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.
Conservative Wasserstein Training for Pose Estimation
We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
MetaGAN: An Adversarial Approach to Few-Shot Learning
In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems.
Combining Model-based and Model-free RL via Multi-step Control Variates
Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance.
Residual Connections Encourage Iterative Inference
In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features.
Boundary-Seeking Generative Adversarial Networks
We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.
Mode Regularized Generative Adversarial Networks
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes.
Architectural Complexity Measures of Recurrent Neural Networks
In this paper, we systematically analyze the connecting architectures of recurrent neural networks (RNNs).
Ranked #22 on
Language Modelling
on Text8
