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Found 50 papers, 18 papers with code
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success.
GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot
Multi-task robot learning holds significant importance in tackling diverse and complex scenarios.
Context-Former: Stitching via Latent Conditioned Sequence Modeling
On the other hand, Decision Transformer (DT) abstracts the decision-making as sequence modeling, showcasing competitive performance on offline RL benchmarks, however, recent studies demonstrate that DT lacks of stitching capability, thus exploit stitching capability for DT is vital to further improve its performance.
QUAR-VLA: Vision-Language-Action Model for Quadruped Robots
Within this framework, a notable challenge lies in aligning fine-grained instructions with visual perception information.
Expressive Forecasting of 3D Whole-body Human Motions
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization.
A dynamical clipping approach with task feedback for Proximal Policy Optimization
Different from previous clipping approaches, we consider increasing the maximum cumulative Return in reinforcement learning (RL) tasks as the preference of the RL task, and propose a bi-level proximal policy optimization paradigm, which involves not only optimizing the policy but also dynamically adjusting the clipping bound to reflect the preference of the RL tasks to further elevate the training outcomes and stability of PPO.
A Short Overview of 6G V2X Communication Standards
We are on the verge of a new age of linked autonomous cars with unheard-of user experiences, dramatically improved air quality and road safety, extremely varied transportation settings, and a plethora of cutting-edge apps.
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation
Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features, including appearance.
RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics.
Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
Offline-to-online RL can make full use of pre-collected offline datasets to initialize policies, resulting in higher sample efficiency and better performance compared to only using online algorithms alone for policy training.
Learning How to Propagate Messages in Graph Neural Networks
To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs.
A General Offline Reinforcement Learning Framework for Interactive Recommendation
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments.
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
To facilitate the development of ORL, we benchmarked 11 ORL algorithms in the realistic quadrupedal locomotion dataset.
VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training.
Learning on Graphs with Out-of-Distribution Nodes
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
Evaluating the Impact of Numerology and Retransmission on 5G NR V2X Communication at A System-Level Simulation
To meet the stringent reliability and latency requirements of C-V2X communication, we suggest and assess a retransmission scheme along with a scheme that incorporates varying resource allocations for retransmission in NR V2X communication.
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Due to the existence of similarity trap, such consistency regularization improperly enhances the consistency possiblity of the model's predictions between segment pairs, and thus reduces the confidence in reward learning, since the augmented distribution does not match with the original one in PbRL.
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level?
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards.
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data.
Beyond Reward: Offline Preference-guided Policy Optimization
Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively.
Offline Experience Replay for Continual Offline Reinforcement Learning
In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks.
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs.
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning
OOD exacerbates inconsistencies in magnitudes and directions of task gradients, which brings challenges for GBML to optimize the meta-knowledge by minimizing the sum of task gradients in each minibatch.
Behavior Proximal Policy Optimization
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments
First, we propose an Environment Representation Graph (ERG) through object detection to express the environment in semantic level.
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.
Can Offline Reinforcement Learning Help Natural Language Understanding?
Experimental results show that our RL pre-trained models can give close performance compared with the models using the LM training objective, showing that there exist common useful features across these two modalities.
KSG: Knowledge and Skill Graph
This is the first study that we are aware of that looks into dynamic KSG for skill retrieval and learning.
Reference-Limited Compositional Zero-Shot Learning
Compositional zero-shot learning (CZSL) refers to recognizing unseen compositions of known visual primitives, which is an essential ability for artificial intelligence systems to learn and understand the world.
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks.
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Specifically, DARA emphasizes learning from those source transition pairs that are adaptive for the target environment and mitigates the offline dynamics shift by characterizing state-action-next-state pairs instead of the typical state-action distribution sketched by prior offline RL methods.
A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control
Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning.
Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning
The deep learning model extracts key points during animal motion from videos.
Learning to Evolve on Dynamic Graphs
In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information.
Mobility prediction Based on Machine Learning Algorithms
Nowadays mobile communication is growing fast in the 5G communication industry.
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy.
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain
To achieve this, the widely used GAN-inspired IRL method is adopted, and its discriminator, recognizing policy-generating trajectories, is modified with the quantification of dynamics difference.
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.
Reference-Limited Compositional Learning: A Realistic Assessment for Human-level Compositional Generalization
To narrow the considerable gap between artificial and human intelligence, we propose a new task, namely reference-limited compositional learning (RLCL), which reproduces three core challenges to mimic human perception: compositional learning, few-shot, and few referential compositions.
Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning
Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both.
Adaptive Adversarial Training for Meta Reinforcement Learning
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task.
Pareto Self-Supervised Training for Few-Shot Learning
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data.
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions.
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning
In the training phase, we first locate the generalization problem to the visual perception module, and then compare two meta-learning algorithms for better generalization in seen and unseen environments.
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks
Domain adaptation using graph-structured networks learns label-discriminative and network-invariant node embeddings by sharing graph parameters.
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation.
Learning transitional skills with intrinsic motivation
By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn useful skills without supervision.
