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Arxiv
2h
253
Image Credit: Arxiv
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models
- Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions.
- Coordinating multiple agents in a shared environment poses challenges in continuous spaces, where traditional optimization algorithms struggle with scalability.
- Diffusion models have shown promise in single-agent path planning, but extending them to MAPF introduces new challenges for constraint feasibility, such as inter-agent collision avoidance.
- To address this, a novel approach is proposed that combines constrained optimization with diffusion models for MAPF in continuous spaces, resulting in feasible multi-agent trajectories that respect collision avoidance and kinematic constraints.
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Arxiv
2h
239
Image Credit: Arxiv
Shifted Composition III: Local Error Framework for KL Divergence
- This paper introduces a shifted composition rule to adapt coupling arguments to the Kullback-Leibler (KL) divergence.
- The framework combines local error analysis and Girsanov's theorem to yield tight bounds and KL divergence guarantees.
- It is applicable in cases of strongly log-concave, weakly log-concave, or log-Sobolev target distributions.
- The results include KL guarantees for the randomized midpoint discretization of the Langevin diffusion.
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Arxiv
2h
39
Image Credit: Arxiv
Combinatorial Regularity for Relatively Perfect Discrete Morse Gradient Vector Fields of ReLU Neural Networks
- A study introduces a schematic for translating between a given piecewise linear Morse function on a canonical polyhedral complex and a compatible discrete Morse function for ReLU neural networks.
- The study expands computational tools for analyzing the topological properties of ReLU neural networks using discrete Morse theory.
- An algorithm is introduced to determine if a given vertex in a canonical polyhedral complex corresponds to a piecewise linear Morse critical point.
- New realizability results are provided for shallow ReLU neural networks with respect to sublevel set topology.
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Arxiv
2h
302
Image Credit: Arxiv
An information theoretic limit to data amplification
- Generative artificial intelligence, such as Generative Adversarial Networks (GANs), has been used to amplify data for scientific analysis, allowing for data generation in reduced computing time.
- The process of data amplification, which violates the principle of getting information for free, can result in a gain factor greater than one while keeping the information content unchanged.
- This study presents a mathematical bound for data amplification, dependent on the number of generated and training events, and determines conditions for ensuring this bound.
- While the resolution of variables in amplified data is not improved, the increase in sample size can improve statistical significance.
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Arxiv
2h
285
Image Credit: Arxiv
Emoji Retrieval from Gibberish or Garbled Social Media Text: A Novel Methodology and A Case Study
- Emojis are often lost in noisy or garbled text on social media, posing challenges for data analysis and machine learning.
- A three-step reverse-engineering methodology is proposed to retrieve emojis from garbled text in social media posts.
- The methodology was applied to 509,248 tweets about the Mpox outbreak, retrieving 157,748 emojis from 76,914 tweets.
- Improvements in text readability and coherence were demonstrated through various metrics, and the usage patterns of individual emojis were analyzed.
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Arxiv
2h
279
Image Credit: Arxiv
Fair Knowledge Tracing in Second Language Acquisition
- This study evaluates the fairness of two predictive models in second-language acquisition using the Duolingo dataset.
- Key findings include the superiority of deep learning over machine learning in terms of accuracy and fairness in second-language knowledge tracing.
- Both models show a bias towards mobile users over non-mobile users.
- Machine learning exhibits stronger bias against developing countries compared to deep learning.
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Arxiv
2h
322
Image Credit: Arxiv
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks
- MMFactory is a universal solution search engine for vision-language tasks.
- It aims to provide a diverse pool of programmatic solutions by instantiating and combining visio-lingual tools from its model repository.
- MMFactory considers user constraints and proposes solutions that meet unique design constraints.
- Experimental results show that MMFactory outperforms existing methods in delivering tailored solutions.
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Arxiv
2h
223
Image Credit: Arxiv
Heterogeneous transfer learning for high dimensional regression with feature mismatch
- Researchers propose a two-stage method for transferring knowledge from a source domain to a target domain in high-dimensional regression models with feature mismatch.
- Existing transfer learning methods assume that the source and target domains have the same feature space, which limits their practical applicability.
- The proposed method involves learning the relationship between missing and observed features in the source domain and solving a joint penalized regression optimization problem in the target domain.
- The researchers provide an upper bound on the parameter estimation risk and prediction risk, assuming sparse differences between the source and target domain parameters.
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Arxiv
2h
207
Image Credit: Arxiv
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner
- Motion planning is crucial in autonomous driving, but curating datasets for training motion planners is expensive and may not capture rare critical scenarios.
- Researchers propose an inexpensive method for generating diverse critical traffic scenarios to train robust motion planners.
- They use scripts to represent traffic scenarios and train a Large Language Model (LLM) to generate scripts from user-specified text descriptions.
- Motion planners trained with the generated synthetic data outperform those trained solely on real-world data.
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Arxiv
2h
118
Image Credit: Arxiv
BRIDGE: Bundle Recommendation via Instruction-Driven Generation
- Bundle recommendation aims to suggest a set of interconnected items to users.
- BRIDGE is a novel framework for bundle recommendation that addresses challenges posed by diverse interaction types and sparse interaction matrices.
- It consists of two main components: correlation-based item clustering and pseudo bundle generation.
- Experimental results validate the superiority of BRIDGE over state-of-the-art ranking-based methods across five benchmark datasets.
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Arxiv
2h
52
Image Credit: Arxiv
Age Optimal Sampling for Unreliable Channels under Unknown Channel Statistics
- This paper discusses the optimization of sampling policies for unreliable channels with unknown channel statistics.
- The system consists of a sensor sending updates to a receiver through an error-prone channel, with transmission results sent back via a reliable channel.
- The objective is to minimize the Age of Information (AoI) metric by designing a sampling policy.
- The proposed algorithm is shown to be effective in minimizing AoI and improving stability.
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3 Likes
Arxiv
2h
105
Image Credit: Arxiv
An Instrumental Value for Data Production and its Application to Data Pricing
- This paper presents an approach for capturing the instrumental value of data production processes.
- The approach considers the context of the agent's decision-making problem and prior data or information possessed.
- In the domain of Bayesian linear regression, the value corresponds to information gain.
- The study shows that achieving first-best revenue for data sales depends on the seller's ability to customize data requests.
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6 Likes
Arxiv
2h
233
Image Credit: Arxiv
Robustness-aware Automatic Prompt Optimization
- BATprompt (By Adversarial Training prompt) is a novel method for prompt generation designed to withstand input perturbations.
- It uses adversarial training techniques to generate prompts that have strong performance on perturbed tasks.
- BATprompt avoids reliance on real gradients or model parameters and leverages the advanced capabilities of Large Language Models (LLMs).
- Experiments show that BATprompt outperforms existing prompt generation methods, delivering robustness and performance under diverse perturbation scenarios.
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Arxiv
2h
285
Image Credit: Arxiv
Quantum framework for Reinforcement Learning: integrating Markov Decision Process, quantum arithmetic, and trajectory search
- This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov Decision Process (MDP).
- The implementation and optimization of agent-environment interactions are done entirely within the quantum domain, eliminating reliance on classical computations.
- Key contributions include quantum-based state transitions, return calculation, and trajectory search mechanisms that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena.
- Experimental results show the capacity of a quantum model to achieve quantum advantage in RL, highlighting the potential of fully quantum implementations in decision-making tasks.
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17 Likes
Arxiv
2h
187
Image Credit: Arxiv
U-Mamba-Net: A highly efficient Mamba-based U-net style network for noisy and reverberant speech separation
- U-Mamba-Net is a lightweight Mamba-based U-style model for speech separation in complex environments.
- The model aims to address the increasing size and computational load of existing speech separation models.
- Mamba is a state space sequence model that incorporates feature selection capabilities.
- U-Mamba-Net achieves improved performance with low computational cost according to the results on Libri2mix.
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