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Found 32 papers, 12 papers with code
Batch Reinforcement Learning with Hyperparameter Gradients
We consider the batch reinforcement learning problem where the agent needs to learn only from a fixed batch of data, without further interaction with the environment.
Dense-SfM: Structure from Motion with Dense Consistent Matching
We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images.
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
Determining the 3D orientations of an object in an image, known as single-image pose estimation, is a crucial task in 3D vision applications.
Body Transformer: Leveraging Robot Embodiment for Policy Learning
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision.
Deflated Dynamics Value Iteration
To accelerate the computation of the value function, we propose Deflated Dynamics Value Iteration (DDVI).
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
To address this limitation, we propose to relax the deterministic target policy using a kernel and learn the kernel metrics that minimize the overall mean squared error of the estimated temporal difference update vector of an action value function, where the action value function is used for policy evaluation.
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
To this end, we introduce AlberDICE, an offline MARL algorithm that alternatively performs centralized training of individual agents based on stationary distribution optimization.
MFOS: Model-Free & One-Shot Object Pose Estimation
Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based.
Tempo Adaptation in Non-stationary Reinforcement Learning
In the context of the time-desynchronized environment, however, the agent at time $t_{k}$ allocates $\Delta t$ for trajectory generation and training, subsequently moves to the next episode at $t_{k+1}=t_{k}+\Delta t$.
SACReg: Scene-Agnostic Coordinate Regression for Visual Localization
Instead of encoding the scene coordinates into the network weights, our model takes as input a database image with some sparse 2D pixel to 3D coordinate annotations, extracted from e. g. off-the-shelf Structure-from-Motion or RGB-D data, and a query image for which are predicted a dense 3D coordinate map and its confidence, based on cross-attention.
Learning Rotation-Equivariant Features for Visual Correspondence
The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension.
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces.
Self-Supervised Learning of Image Scale and Orientation
We study the problem of learning to assign a characteristic pose, i. e., scale and orientation, for an image region of interest.
Self-Supervised Equivariant Learning for Oriented Keypoint Detection
Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching.
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset.
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
We consider the problem of learning from observation (LfO), in which the agent aims to mimic the expert's behavior from the state-only demonstrations by experts.
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
The tremendous recent progress in analyzing the training dynamics of overparameterized neural networks has primarily focused on wide networks and therefore does not sufficiently address the role of depth in deep learning.
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
We consider offline imitation learning (IL), which aims to mimic the expert's behavior from its demonstration without further interaction with the environment.
Offline Reinforcement Learning for Large Scale Language Action Spaces
GPT-Critic is essentially free from the issue of diverging from human language since it learns from the sentences sampled from the pre-trained language model.
Rotation-Equivariant Keypoint Detection
Therefore, we propose a rotation-invariant keypoint detection method using rotation-equivariant CNNs.
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
We consider the offline reinforcement learning (RL) setting where the agent aims to optimize the policy solely from the data without further environment interactions.
A passively pumped vacuum package sustaining cold atoms for more than 200 days
Compact cold-atom sensors depend on vacuum technology.
Atomic Physics Applied Physics
Monte-Carlo Planning and Learning with Language Action Value Estimates
Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space.
Representation Balancing Offline Model-based Reinforcement Learning
We present a new objective for model learning motivated by recent advances in the estimation of stationary distribution corrections.
Reinforcement Learning for Control with Multiple Frequencies
Many real-world sequential decision problems involve multiple action variables whose control frequencies are different, such that actions take their effects at different periods.
Learning to Compose Hypercolumns for Visual Correspondence
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers.
Ranked #2 on
Semantic correspondence
on Caltech-101
Spherical Principal Curves
In this study, we propose a new approach to construct principal curves on a sphere by a projection of the data onto a continuous curve.
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems.
SPair-71k: A Large-scale Benchmark for Semantic Correspondence
In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70, 958 image pairs with diverse variations in viewpoint and scale.
Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details.
Ranked #1 on
Semantic correspondence
on Caltech-101
Monte-Carlo Tree Search for Constrained POMDPs
In this paper, we present CC-POMCP (Cost-Constrained POMCP), an online MCTS algorithm for large CPOMDPs that leverages the optimization of LP-induced parameters and only requires a black-box simulator of the environment.
Attentive Semantic Alignment with Offset-Aware Correlation Kernels
Semantic correspondence is the problem of establishing correspondences across images depicting different instances of the same object or scene class.
