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Arxiv
18h
218
Image Credit: Arxiv
A Bilevel Optimization Framework for Imbalanced Data Classification
- Researchers propose a new undersampling approach to tackle imbalanced data classification issues by avoiding synthetic data pitfalls and under-fitting.
- Their method selects datapoints based on their potential to improve model loss rather than randomly undersampling majority data.
- The approach aims to identify an optimal subset of majority training data by rejecting redundant datapoints, leveraging a bilevel optimization problem.
- Experimental results demonstrate F1 scores up to 10% higher compared to existing state-of-the-art methods.
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Arxiv
18h
59
Image Credit: Arxiv
A Multi-Granularity Supervised Contrastive Framework for Remaining Useful Life Prediction of Aero-engines
- Accurate remaining useful life (RUL) predictions are crucial for safe operation of aero-engines.
- This paper introduces a multi-granularity supervised contrastive (MGSC) framework to address limitations in current RUL prediction methods.
- The MGSC framework aims to align samples with the same RUL label in the feature space, improving prediction accuracy.
- The proposed strategy is implemented on the CMPASS dataset and enhances RUL prediction accuracy using a convolutional long short-term memory network as a baseline.
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Arxiv
18h
181
Image Credit: Arxiv
Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges
- Semantic Edge Computing (SEC) and Semantic Communications (SemComs) are being explored for real-time intelligence in 6G wireless networks.
- SemCom utilizes Deep Neural Networks (DNNs) to encode and communicate semantic information while SEC uses distributed DNNs to optimize computing across devices.
- This work aims to bridge the gap between SEC and SemCom by analyzing research problems, technical strengths, and challenges in both fields.
- The study provides a comprehensive overview of the current state of the art in SEC and SemCom for 6G networks.
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Arxiv
18h
243
Image Credit: Arxiv
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning
- Recent research explores the combination of non-uniform exploration and supervised learning in decision-making systems to improve immediate performance while maintaining off-policy learning capabilities.
- An analysis conducted at Adyen, a global payments processor, demonstrates that regression oracles can enhance system performance but may introduce challenges due to rigid algorithmic assumptions.
- The study reveals that improvements in policy may lead to subsequent performance degradation due to shifts in reward distribution and increased class imbalance in training data.
- There is a potential 'oscillation effect' identified where regression oracles influence probability estimates, impacting the stability and performance consistency of policy models over successive iterations.
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14 Likes
Arxiv
18h
251
Image Credit: Arxiv
Challenges learning from imbalanced data using tree-based models: Prevalence estimates systematically depend on hyperparameters and can be upwardly biased
- Imbalanced binary classification problems are common in various fields of study.
- Subsampling the majority class to create a balanced training dataset can bias the model's predictions.
- Calibrating a random forest model using prevalence estimates can lead to unintended negative consequences, including upwardly biased estimates.
- Random forests' prevalence estimates depend on the number of predictors considered at each split and the sampling rate used, revealing unexpected biases.
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Arxiv
18h
362
Image Credit: Arxiv
"I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models
- The paper discusses challenges in machine learning safety introduced by multimodal large language models, with a focus on Audio-Language Models (ALMs).
- It explores audio jailbreaks targeting ALMs, showing the first universal jailbreaks in the audio modality that can bypass alignment mechanisms and remain effective in simulated real-world conditions.
- The research reveals that adversarial perturbations encode imperceptible toxic speech, suggesting that embedding linguistic features within audio signals can elicit toxic outputs.
- The study highlights the importance of understanding interactions between modalities in multimodal models and provides insights to improve defenses against adversarial audio attacks.
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21 Likes
Arxiv
18h
258
Image Credit: Arxiv
Parametric Scaling Law of Tuning Bias in Conformal Prediction
- Conformal prediction is a framework for uncertainty quantification that constructs prediction sets with coverage guarantees, often requiring a holdout set for parameter tuning.
- Empirical findings suggest that the tuning bias, resulting from using the same dataset for tuning and calibration, is minimal for simple parameter tuning in many conformal prediction methods.
- A scaling law for tuning bias is observed, showing that bias increases with parameter space complexity but decreases with calibration set size.
- The study establishes a theoretical framework to quantify tuning bias, provides a proof for the scaling law, and discusses strategies to mitigate tuning bias based on the research findings.
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Arxiv
18h
70
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ARBoids: Adaptive Residual Reinforcement Learning With Boids Model for Cooperative Multi-USV Target Defense
- Researchers introduced ARBoids, an adaptive residual reinforcement learning framework for the target defense problem with unmanned surface vehicles (USVs).
- ARBoids integrates deep reinforcement learning (DRL) with the Boids model for multi-agent coordination in challenging interception scenarios.
- In simulations, ARBoids demonstrated superior performance compared to traditional interception strategies and showed adaptability to attackers with varying maneuverability.
- The code for ARBoids will be made available upon the acceptance of this research letter.
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Arxiv
18h
303
Image Credit: Arxiv
Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition
- Online handwriting recognition (HWR) using data from inertial measurement units (IMUs) poses challenges due to writing style variations and limited annotated datasets.
- This paper introduces an HWR model focused on improving writer-independent (WI) recognition on IMU data, employing a CNN encoder and a BiLSTM-based decoder.
- The model exhibits robustness to unseen handwriting styles, surpassing existing methods on WI splits of public datasets with low character error rates (CERs) and word error rates (WERs).
- Extensive evaluation demonstrates its adaptability to different age groups and efficiency through design choices, hinting at the potential for more adaptable and scalable HWR systems.
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18 Likes
Arxiv
18h
332
Image Credit: Arxiv
Deep Learning is Not So Mysterious or Different
- Deep neural networks are often viewed as different due to their generalization behavior, including benign overfitting and double descent.
- The anomalies observed in neural networks can be understood using traditional generalization frameworks like PAC-Bayes.
- The concept of soft inductive biases is key in explaining neural networks' generalization behavior, advocating for a flexible hypothesis space.
- While deep learning shares commonalities with other model classes, it stands out in representation learning and universality.
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20 Likes
Arxiv
18h
96
Image Credit: Arxiv
Mixture of Group Experts for Learning Invariant Representations
- A new perspective on Mixture-of-Experts (MoE) models with top-k routing has been introduced, called Mixture of Group Experts (MoGE), to address limitations of vanilla MoE models.
- MoGE utilizes group sparse regularization for routing inputs, creating a 2D topographic map that enhances expert diversity and specialization, leading to improved performance in tasks like image classification and language modeling.
- Comprehensive evaluations show that MoGE outperforms traditional MoE models with minimal extra memory and computation requirements, offering an efficient solution to scale the number of experts while avoiding redundancy.
- The source code for MoGE is included in the supplementary material and will be made publicly available for further exploration and implementation.
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5 Likes
Arxiv
18h
184
Image Credit: Arxiv
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
- This paper explores a cryptographic perspective on the mitigation versus detection of adversarial inputs in machine learning algorithms during inference time.
- The study introduces defense by detection (DbD) and defense by mitigation (DbM) concepts, where correctness, completeness, and soundness properties are defined to ensure successful defense without significantly impacting algorithm performance.
- The research indicates that achieving DbD and DbM is equivalent for machine learning classification tasks but differs for generative learning tasks, showcasing scenarios where mitigation is possible but detection is not provable.
- The findings rely on cryptographic tools like Identity-Based Fully Homomorphic Encryption (IB-FHE) and Non-Parallelizing Languages with Average-Case Hardness (NPL) to demonstrate the feasibility of defending by mitigation under certain assumptions.
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11 Likes
Arxiv
18h
288
Image Credit: Arxiv
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
- Saliency methods, used for visual validation in image and language processing, face challenges when applied to time series data due to its complexity and diverse nature.
- A study investigated various saliency methods on time series data to provide recommendations for interpreting convolutional models, particularly focusing on the tool-use time series dataset.
- The study used nine different post-hoc saliency methods on six real-world datasets, evaluating them with five metrics to offer guidance on choosing suitable methods for interpreting models.
- Results showed that no single saliency method consistently outperformed others across all metrics, but the study provides insights and guidelines to help experts select appropriate methods for specific models and datasets.
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17 Likes
Arxiv
18h
181
Image Credit: Arxiv
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
- The study focuses on parameter estimation in high-dimensional generalized linear models.
- They propose spectral methods using the principal eigenvector of a data-dependent matrix for effective solutions.
- The research addresses the challenges of structured real-world data matrices with non-trivial correlations.
- Their approach provides a precise performance characterization and optimal preprocessing for parameter estimation.
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10 Likes
Arxiv
18h
184
Image Credit: Arxiv
Unsupervised Morphological Tree Tokenizer
- Tokenization is crucial in language modeling to segment text inputs into atomic units.
- A new deep model has been introduced to incorporate morphological structure guidance into tokenization.
- The model utilizes a mechanism called $ extit{MorphOverriding}$ to maintain the indecomposability of morphemes and align with morphological rules.
- Empirical results show that the proposed method outperforms traditional methods like BPE and WordPiece in morphological segmentation and language modeling tasks.
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11 Likes
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