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Arxiv
2d
138
Image Credit: Arxiv
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
2d
281
Image Credit: Arxiv
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
2d
122
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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
2d
178
Image Credit: Arxiv
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
2d
162
Image Credit: Arxiv
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
2d
19
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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
2d
257
Image Credit: Arxiv
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
2d
368
Image Credit: Arxiv
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
2d
27
Image Credit: Arxiv
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
2d
261
Image Credit: Arxiv
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
2d
241
Image Credit: Arxiv
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
2d
257
Image Credit: Arxiv
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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Arxiv
2d
368
Image Credit: Arxiv
Which LIME should I trust? Concepts, Challenges, and Solutions
- Explainable Artificial Intelligence (XAI) is crucial for fostering trust and detecting potential misbehavior of opaque models.
- LIME (Local Interpretable Model-agnostic Explanations) is a popular model-agnostic approach for generating explanations of black-box models.
- LIME faces challenges related to fidelity, stability, and applicability to domain-specific problems.
- A survey has been conducted to comprehensively explore and collect LIME's foundational concepts and known limitations, categorize and compare its enhancements, and offer a structured taxonomy for future research and practical application.
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Arxiv
2d
75
Image Credit: Arxiv
Effectively Controlling Reasoning Models through Thinking Intervention
- Reasoning-enhanced large language models (LLMs) generate intermediate reasoning steps prior to generating final answers, excelling in complex problem-solving.
- Thinking Intervention is a novel paradigm designed to guide the internal reasoning processes of LLMs by strategically inserting or revising specific thinking tokens.
- Comprehensive evaluations show that Thinking Intervention outperforms baseline prompting approaches, achieving significant improvements in instruction following, instruction hierarchy, and safety alignment tasks.
- The research on Thinking Intervention offers a promising new avenue for controlling reasoning LLMs.
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4 Likes
Arxiv
2d
150
Image Credit: Arxiv
Prompt, Divide, and Conquer: Bypassing Large Language Model Safety Filters via Segmented and Distributed Prompt Processing
- Researchers have developed a framework to bypass safety filters of large language models (LLMs) and generate malicious code.
- The framework employs distributed prompt processing and iterative refinements to achieve a 73.2% success rate (SR) in generating malicious code.
- Comparative analysis shows that traditional single-LLM judge evaluation overestimates SRs compared to the LLM jury system.
- The distributed architecture improves SRs by 12% compared to the non-distributed approach.
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