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Arxiv
3d
68
Image Credit: Arxiv
Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution
- This work focuses on table integration in data lakes to consolidate relevant information.
- The core tasks investigated are pairwise integrability judgment, integrable set discovery, and multi-tuple conflict resolution.
- A binary classifier is trained using a self-supervised adversarial contrastive learning algorithm to address pairwise integrability judgment.
- An innovative in-context learning methodology is introduced to effectively resolve conflicts during multi-tuple integration.
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Arxiv
3d
16
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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis
- This paper discusses the problem of social harms in multi-agent reinforcement learning.
- It proposes market-based mechanisms to measure and control the cost of social harms.
- The setup captures a wide range of scenarios and allows for different learning strategies.
- It provides practical applications, such as the Paperclips problem and pollution control.
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Arxiv
3d
276
Image Credit: Arxiv
Navigation World Models
- Researchers have introduced a Navigation World Model (NWM), a controllable video generation model for predicting future visual observations based on past observations and navigation actions.
- NWM employs a Conditional Diffusion Transformer (CDiT) with 1 billion parameters, trained on a diverse collection of egocentric videos of human and robotic agents.
- In familiar environments, NWM can plan navigation trajectories by simulating and evaluating them for achieving the desired goal, incorporating constraints dynamically during planning.
- NWM can also imagine trajectories in unfamiliar environments using learned visual priors from a single input image, making it a versatile tool for next-generation navigation systems.
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Arxiv
3d
272
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Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification
- Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to make highly confident decisions while abstaining when uncertainty is high.
- The goal of this study is to minimize the number of indecisions, which are observations that are not automated, while achieving a target classification accuracy.
- The study provides a full characterization of the minimax risk in selective classification, proving key properties and enabling optimal indecision selection.
- The findings highlight the potential of selective classification to significantly reduce misclassification rates with a relatively small cost in terms of indecisions.
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Arxiv
3d
268
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Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
- This work discusses the impact of missing outcome data on the estimation of treatment effects.
- The authors propose two de-biased machine learning estimators for the conditional average treatment effect (CATE).
- The mDR-learner and mEP-learner integrate inverse probability of censoring weights to address under-representation.
- The performance of these estimators is illustrated through simulated data settings and compared to existing CATE estimators.
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Arxiv
3d
196
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Probing Visual Language Priors in VLMs
- Probing Visual Language Priors in VLMs
- A new benchmark called ViLP is introduced to investigate the reliance of Vision-Language Models (VLMs) on visual language priors.
- ViLP consists of out-of-distribution images and associated Q&A pairs, which require true visual reasoning rather than text priors.
- A self-improving framework is proposed to enhance VLM performance by generating new VQA data and applying corruptions to emphasize actual visual inputs.
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Arxiv
3d
124
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Beyond Words: AuralLLM and SignMST-C for Sign Language Production and Bidirectional Accessibility
- Sign language is the primary communication mode for 72 million hearing-impaired individuals worldwide.
- Introducing CNText2Sign and CNSign, the first unified dataset supporting bidirectional accessibility systems for Chinese sign language.
- AuraLLM model offers direct gesture accuracy assessment using pose data and achieves controllable coordination of gestures and facial expressions.
- SignMST-C achieves new SOTA results on PHOENIX2014-T dataset for Sign Language Translation with BLEU-4 scores up to 32.08.
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Arxiv
3d
68
Image Credit: Arxiv
Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities
- Verifying the provenance of content is crucial to the function of many organizations, especially in the context of LLM-generated text.
- Researchers have developed zero-shot statistical tests to determine if a piece of text was produced by a specific LLM or by a human.
- The tests demonstrate high accuracy and error reduction as the text length increases.
- The findings have practical implications for identifying the origin of harmful or false LLM-generated text.
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Arxiv
3d
320
Image Credit: Arxiv
Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning
- This paper focuses on transfer learning for dynamic decision scenarios modeled by non-stationary finite-horizon Markov decision processes.
- The authors propose a novel re-weighted targeting procedure to construct transferable RL samples and introduce transfer deep Q*-learning.
- The method utilizes neural network approximation with theoretical guarantees and can handle transferable and non-transferable reward functions and transition densities.
- Empirical experiments on synthetic and real datasets demonstrate the effectiveness of the proposed method in non-stationary reinforcement learning contexts.
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Arxiv
3d
184
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Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework
- Automated Vehicles (AVs) aim to improve road safety and traffic efficiency.
- Research is needed to develop Socially Compliant AVs (SCAVs) for successful integration into mixed traffic.
- A comprehensive scoping review and expert interviews have been conducted to assess the state of the art in developing SCAVs.
- A conceptual framework for the development of SCAVs has been proposed and validated through an online survey.
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Arxiv
3d
368
Image Credit: Arxiv
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
- Researchers propose a coreset-based task selection approach for sample-efficient meta-reinforcement learning (MAML-RL).
- The approach selects a weighted subset of tasks based on their diversity in gradient space, reducing task redundancy.
- Task selection accelerates adaptation to unseen tasks and focuses training on relevant tasks.
- The proposed approach shows sample complexity reduction in MAML-LQR and improves performance across multiple RL benchmark problems.
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Arxiv
3d
48
Image Credit: Arxiv
Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach
- This paper focuses on energy-efficient communication in next-generation Internet of Things (NG-IoT) networks.
- The authors deployed flying LoRa gateways mounted on unmanned aerial vehicles (UAVs).
- They aim to maximize the global system energy efficiency of wireless LoRa networks through joint optimization.
- Their proposed method, using a cooperative multi-agent reinforcement learning approach, outperforms conventional schemes.
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Arxiv
3d
51
Image Credit: Arxiv
Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
- This paper proposes a State-Augmentation (SA) based distributed optimization approach for packet-based information routing in wireless communication networks.
- The approach leverages Graph Neural Networks (GNNs) to extract routing policies based on network node connections.
- Numerical experiments show that the proposed method outperforms baseline algorithms and is effective in real-world network topologies.
- The method enables opportunistic routing, improving the handling of information by source nodes in the network.
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Arxiv
3d
216
Image Credit: Arxiv
Fourier Sliced-Wasserstein Embedding for Multisets and Measures
- We present the Fourier Sliced-Wasserstein (FSW) embedding - a novel method to embed multisets and measures over R^d into Euclidean space.
- The FSW embedding approximately preserves the sliced Wasserstein distance on distributions, resulting in meaningful representations that capture the input structure.
- Unlike other methods, the FSW embedding is bi-Lipschitz on multisets and injective on measures, offering significant advantages over sum- or max-pooling techniques.
- Numerical experiments confirm the superiority of FSW embedding in practical learning tasks, achieving state-of-the-art performance in learning Wasserstein distance and improved robustness in PointNet with reduced parameters.
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Medium
3d
28
G(I)RWM — Machine Learning Edition | No Data?
- Machine learning models require specific types of data as fuel.
- Understanding the type, structure, and representation of data is crucial for model learning.
- Encoding is used to translate non-numerical data into machine-readable format.
- Choosing the right data type and encoding strategy is essential for accurate model training.
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