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Arxiv
4d
234
Image Credit: Arxiv
TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models
- TalkWithMachines aims to enhance human-robot interaction in interpretable industrial robotics.
- The paper explores the integration of Large Language Models (LLMs) and Vision Language Models (VLMs) with robotic perception and control.
- This enables robots to understand and execute commands in natural language and perceive their environment through visual and descriptive inputs.
- The research focuses on four LLM-assisted simulated robotic control workflows, including low-level control, language-based feedback, visual information usage, and task planning.
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Arxiv
4d
105
Image Credit: Arxiv
Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data
- Educational stakeholders are interested in sparse, delayed student outcomes like end-of-year statewide exams.
- Prior work has focused on using long-term usage data to predict outcomes, but this study investigates using short-term log data to predict students' end-of-school year assessments.
- The study utilizes datasets from students in Uganda using a literacy game product and students in the US using two mathematics tutoring systems.
- Findings suggest that 2-5 hours of log usage data can provide valuable insight into students' long-term performance.
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Arxiv
4d
263
Image Credit: Arxiv
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game
- Researchers propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments.
- DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation.
- At the lower-level, DualGFL introduces a new auction-aware utility function and a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles.
- At the upper-level, DualGFL formulates a multi-attribute auction game with resource constraints and derives equilibrium bids to maximize coalitions' winning probabilities and profits.
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Arxiv
4d
348
Image Credit: Arxiv
The Impact of Cut Layer Selection in Split Federated Learning
- Split Federated Learning (SFL) combines federated learning and split learning.
- SFL partitions a neural network at a cut layer, with initial layers on clients and remaining layers on a training server.
- SFL-V1 maintains separate server-side models for each client, while SFL-V2 maintains a single shared model for all clients.
- Cut layer selection significantly affects the performance of SFL-V2, outperforming FedAvg on certain datasets.
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Arxiv
4d
218
Image Credit: Arxiv
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
- Researchers have introduced the Nutritional Graph Question Answering (NGQA) benchmark
- NGQA is the first graph question answering dataset designed for personalized nutritional health reasoning
- The benchmark leverages data from the National Health and Nutrition Examination Survey (NHANES) and the Food and Nutrient Database for Dietary Studies (FNDDS)
- NGQA effectively challenges existing models and advances GraphQA research with a novel domain-specific benchmark
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Arxiv
4d
400
Image Credit: Arxiv
Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings
- Recent studies have shown that artificial neural network (ANN) representations can resemble cortical representations when exposed to the same auditory inputs.
- This study proposes a new approach by using ANN representations as a supervisory signal to train recognition models for music identification using non-invasive brain recordings.
- By training an EEG recognition model to predict ANN representations associated with music identification, significant improvement in classification accuracy is observed.
- This research has potential implications for advancing brain-computer interfaces, neural decoding techniques, and our understanding of music cognition.
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Arxiv
4d
388
Image Credit: Arxiv
In-context Continual Learning Assisted by an External Continual Learner
- Existing continual learning (CL) methods rely on fine-tuning or adapting large language models (LLMs) but suffer from catastrophic forgetting (CF).
- In-context learning (ICL) can leverage the extensive knowledge within LLMs for CL without updating any parameters.
- However, scaling ICL becomes challenging as the prompt length increases and exceeds the input token limit.
- To address this, the InCA approach integrates an external continual learner (ECL) with ICL, resulting in scalable CL without CF and achieving significant performance gains.
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23 Likes
Arxiv
4d
263
Image Credit: Arxiv
Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems
- Assignment problems are combinatorial optimization problems where agents need to be assigned to tasks while maximizing utility and satisfying constraints.
- Multi-agent reinforcement learning (MARL) is applied to solve assignment problems that unfold over time.
- The algorithm uses bootstrapping from a polynomial-time greedy solver and further experience to learn the value of assignments.
- The distributed optimal assignment mechanism is employed to choose assignments.
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Arxiv
4d
247
Image Credit: Arxiv
SaliencyI2PLoc: saliency-guided image-point cloud localization using contrastive learning
- SaliencyI2PLoc is a contrastive learning based architecture for image to point cloud global localization.
- It addresses the modality gap between images and point clouds and maintains feature alignment and relation consistency.
- Saliency map is fused into feature aggregation for more representative global features.
- Experiments show significant improvement in cross-modality global localization compared to baseline methods.
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Arxiv
4d
238
Image Credit: Arxiv
Score-based Generative Diffusion Models for Social Recommendations
- Score-based Generative Diffusion Models for Social Recommendations
- This paper addresses the challenge of low social homophily in social recommendations.
- They propose the Score-based Generative Model for Social Recommendation (SGSR) which adapts Stochastic Differential Equation (SDE) based diffusion models.
- Experiments show that SGSR effectively filters redundant social information and improves recommendation performance.
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Arxiv
4d
121
Image Credit: Arxiv
Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge
- Dexterous manipulation has been a focus of recent research.
- Existing studies have primarily used reinforcement learning methods for hand movements, but these methods are often inefficient and inaccurate.
- This work introduces a novel reinforcement learning approach that utilizes prior dexterous grasp pose knowledge to improve efficiency and accuracy.
- The manipulation process is divided into two phases: generating a dexterous grasp pose targeting the functional part of the object, and using reinforcement learning to explore the environment.
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7 Likes
Arxiv
4d
352
Image Credit: Arxiv
Microservices-Based Framework for Predictive Analytics and Real-time Performance Enhancement in Travel Reservation Systems
- The paper presents a microservices-based framework for enhancing the performance of real-time travel reservation systems using predictive analytics.
- The framework adopts a modularization approach to decouple system components into independent services that can scale according to demand.
- It includes real-time predictive analytics through machine learning models to optimize customer demand forecasting, dynamic pricing, and system performance.
- Experimental evaluation shows that the framework improves performance metrics such as response time, throughput, transaction success rate, and prediction accuracy.
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21 Likes
Arxiv
4d
89
Image Credit: Arxiv
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning
- Agents in multi-agent reinforcement learning (MARL) struggle to assess the relevance of input information for cooperative tasks.
- In communication-limited scenarios, agents are unable to access global information, limiting their collaboration abilities.
- A novel cooperative MARL framework based on information selection and tacit learning is introduced.
- The framework enables agents to develop implicit coordination and adaptively filter information for enhanced decision-making capabilities.
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5 Likes
Arxiv
4d
242
Image Credit: Arxiv
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
- Reinforcement learning methods have proposed promising traffic signal control policy that can be trained on large road networks.
- MacLight, Multi-Scene Aggregation Convolutional Learning, offers faster training speeds and more stable performance.
- It utilizes variational autoencoders for global representation and proximal policy optimization algorithm for value evaluation.
- Experimental results demonstrate superior stability, optimized convergence levels, and the highest time efficiency.
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Arxiv
4d
267
Image Credit: Arxiv
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
- The study investigates the impact of recurrency in existing image segmentation models.
- Different types of recurrency, including self-organizing, relational, and memory retrieval, are explored.
- Experiments are conducted on artificial and medical imaging data with high levels of noise and few-shot learning settings.
- The results do not support the hypothesis that recurrent models perform better in these settings, suggesting further research is needed.
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