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Found 12 papers, 5 papers with code
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model
Recently, some research show that deep neural networks are vulnerable to the adversarial attacks, the well-trainned samples or patches could be used to trick the neural network detector or human visual perception.
Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
To address this problem, we propose Primal Wasserstein DICE (PW-DICE), which minimizes the primal Wasserstein distance between the expert and learner state occupancies with a pessimistic regularizer and leverages a contrastively learned distance as the underlying metric for the Wasserstein distance.
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents.
Ranked #2 on
Code Generation
on HumanEval
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
Neural-PBIR Reconstruction of Shape, Material, and Illumination
In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination.
Ranked #1 on
Depth Prediction
on Stanford-ORB
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
To better benefit from available demonstrations, we develop a method to Combine Explicit and Implicit Priors (CEIP).
A Surrogate Objective Framework for Prediction+Programming with Soft Constraints
Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.
A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints
Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
DML-GANR: Deep Metric Learning With Generative Adversarial Network Regularization for High Spatial Resolution Remote Sensing Image Retrieval
With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing images (HSR-RSIs) increases considerably.
Deriving canonical differential equations for Feynman integrals from a single uniform weight integral
Differential equations are a powerful tool for evaluating Feynman integrals.
High Energy Physics - Phenomenology High Energy Physics - Theory Mathematical Physics Mathematical Physics
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory
Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications.
