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Arxiv

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TransMamba: Flexibly Switching between Transformer and Mamba

  • TransMamba is a framework that combines Transformer and Mamba models for efficient long-sequence processing.
  • TransMamba uses shared parameter matrices to switch between attention and state space model (SSM) mechanisms.
  • The framework includes a Memory converter to bridge Transformer and Mamba models for seamless information flow.
  • Experimental results demonstrate that TransMamba achieves superior training efficiency and performance compared to baselines.

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Arxiv

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Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning

  • This work focuses on generating balanced levels tailored to asymmetric player archetypes in games.
  • The goal is to balance the disparity in abilities through the level design.
  • A method using reinforcement learning is used to balance tile-based game levels.
  • The evaluation shows that the method can balance a larger proportion of levels compared to two baseline approaches.

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Arxiv

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CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning

  • CTSketch is a novel, scalable neurosymbolic learning algorithm for training neural networks using end-to-end input-output labels.
  • CTSketch decomposes the symbolic program into sub-programs and summarizes each sub-program with a sketched tensor to improve scalability.
  • The algorithm approximates the output distribution of the program using simple tensor operations over input distributions and summaries.
  • CTSketch achieves high accuracy on tasks involving over one thousand inputs, pushing neurosymbolic learning to new scales.

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Arxiv

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Learning a Canonical Basis of Human Preferences from Binary Ratings

  • Recent advances in generative AI have been driven by alignment techniques such as reinforcement learning from human feedback (RLHF).
  • This paper focuses on understanding the preferences encoded in datasets used for RLHF and identifying common human preferences.
  • A small subset of 21 preference categories captures over 89% of preference variation across individuals, serving as a canonical basis of human preferences.
  • The identified preference basis proves useful for model evaluation and training, offering insights into model alignment and successful fine-tuning.

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Arxiv

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Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning

  • Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) being the most common subtype.
  • A new study has developed a multimodal machine learning model to predict patient resistance to osimertinib, a third-generation EGFR-tyrosine kinase inhibitor, in late-stage NSCLC patients with activating EGFR mutations.
  • The model achieved a c-index of 0.82 on a multi-institutional dataset by integrating various data types such as histology images, next-generation sequencing (NGS) data, demographics data, and clinical records.
  • The multimodal model demonstrated superior performance over single modality models, highlighting the importance of combining multiple data types for accurate patient outcome prediction.

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Arxiv

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Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantees via Constrained Mean-Field Reinforcement Learning

  • The rapid expansion of ride-sourcing services presents operational challenges, such as vehicle rebalancing.
  • A scalable mean-field control and reinforcement learning model is proposed for precise vehicle repositioning.
  • An accessibility constraint is integrated to ensure equitable service distribution.
  • Empirical evaluation using real-world data-driven simulation demonstrates the efficiency and robustness of the approach.

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Arxiv

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Many-to-Many Matching via Sparsity Controlled Optimal Transport

  • Many-to-many matching seeks to match multiple points in one set and multiple points in another set.
  • This paper proposes a novel many-to-many matching method that explicitly encodes many-to-many constraints while preventing one-to-one matching.
  • The method includes matching budget constraints and a deformed $q$-entropy regularization to maximize the matching budget.
  • Experimental results show that the proposed method achieves good performance in generating meaningful many-to-many matchings.

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Arxiv

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Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing

  • Accurate spatial-temporal prediction of network-based travelers' requests is crucial for the effective policy design of ridesharing platforms.
  • This paper introduces a novel prediction model, OD-CED, for fine-grained Origin-Destination (OD) demand prediction in ridesharing platforms.
  • OD-CED combines an unsupervised space coarsening technique and an encoder-decoder architecture to capture both semantic and geographic dependencies.
  • Experimental results show that OD-CED outperforms traditional statistical methods, achieving significant reductions in root-mean-square error and weighted mean absolute percentage error.

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Arxiv

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Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review

  • Financial institutions are required to monitor vast amounts of transactions for money laundering.
  • Traditional machine learning models have limitations in adapting to dynamic environments for AML detection.
  • Continual graph learning approaches can enhance AML practices by incorporating new information while retaining prior knowledge.
  • Experimental evaluations show that continual learning improves model adaptability and robustness in detecting money laundering.

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Arxiv

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Evaluating and Designing Sparse Autoencoders by Approximating Quasi-Orthogonality

  • Researchers propose a new evaluation metric called Approximate Feature Activation (AFA) for assessing alignment between inputs and activations in Sparse Autoencoders (SAEs).
  • The study introduces a novel SAE architecture called top-AFA SAE, which eliminates the need to tune SAE sparsity hyperparameters.
  • The top-AFA SAEs achieve reconstruction loss comparable to state-of-the-art top-k SAEs without requiring the hyperparameter k to be tuned.
  • The proposed method also introduces the ZF plot, revealing a relationship between large language model hidden embeddings and SAE feature vectors.

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Arxiv

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Value of Information-based Deceptive Path Planning Under Adversarial Interventions

  • Existing methods for deceptive path planning (DPP) do not address the problem of adversarial interventions.
  • A novel Markov decision process (MDP)-based model is proposed for DPP under adversarial interventions.
  • New value of information (VoI) objectives are developed to guide DPP policy design.
  • Computationally efficient methods are derived for synthesizing policies for DPP under adversarial interventions.

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Arxiv

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Evaluating machine learning models for predicting pesticides toxicity to honey bees

  • Small molecules play a critical role in the biomedical, environmental, and agrochemical domains.
  • This work focuses on ApisTox, the most comprehensive dataset of experimentally validated chemical toxicity to the honey bee (Apis mellifera).
  • The evaluation of ApisTox using various machine learning approaches reveals that it represents a distinct chemical space.
  • The limited generalizability of current state-of-the-art algorithms trained solely on biomedical data highlights the need for targeted model development in the agrochemical domain.

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Arxiv

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NoProp: Training Neural Networks without Back-propagation or Forward-propagation

  • The paper introduces a new learning method named NoProp, which does not rely on either forward or backward propagation in deep learning.
  • NoProp takes inspiration from diffusion and flow matching methods to independently learn to denoise a noisy target at each layer.
  • The method demonstrates superior accuracy, ease of use, and computational efficiency compared to other back-propagation-free methods on image classification benchmarks such as MNIST, CIFAR-10, and CIFAR-100.
  • NoProp alters the traditional gradient-based learning paradigm, enabling more efficient distributed learning and potentially impacting other characteristics of the learning process.

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Arxiv

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ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion

  • Parameter generation has emerged as a novel paradigm for neural network development, offering an alternative to traditional neural network training by synthesizing high-quality model weights directly.
  • In this paper, a novel conditional recurrent diffusion framework called ORAL is introduced, which addresses the limitations of existing methods in achieving scalability and controllability.
  • ORAL incorporates a novel conditioning mechanism to generate task-specific Low-Rank Adaptation (LoRA) parameters that can seamlessly transfer across evolving language models.
  • Extensive experiments show that ORAL generates high-quality LoRA parameters, achieving comparable or superior performance to vanilla trained counterparts across various language, vision, and multimodal tasks.

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Arxiv

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SQuat: Subspace-orthogonal KV Cache Quantization

  • Researchers propose SQuat (Subspace-orthogonal KV cache quantization) to reduce memory usage in key-value (KV) cache used for LLMs decoding.
  • SQuat constructs a subspace spanned by query tensors to capture critical task-related information.
  • SQuat enforces orthogonality between (de)quantized and original keys in the subspace, minimizing the impact of quantization errors.
  • The method achieves reduced memory usage, improved throughput, and better benchmark scores compared to existing KV cache quantization algorithms.

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