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21 Nov

TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs

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[Submitted on 14 Jun 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs, by Zhuofeng Li and 8 other authors View PDF HTML (experimental) Abstract:Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between…

21 Nov

ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification

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[Submitted on 2 Aug 2024 (v1), last revised 20 Nov 2024 (this version, v5)] View a PDF of the paper titled ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification, by Juyoung Yun View PDF HTML (experimental) Abstract:The rapid advancements in deep learning necessitate better training methods for deep neural networks (DNNs). As models grow in complexity, vanishing and exploding gradients impede performance, particularly in skip-connected architectures like Deep Residual Networks. We propose Z-Score Normalization for Gradient Descent (ZNorm), an innovative technique that adjusts only the gradients without modifying the network architecture to accelerate training and improve model…

21 Nov

N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2

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[Submitted on 23 Aug 2024 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2, by Hyo Jong Chung and 2 other authors View PDF HTML (experimental) Abstract:Driver motion recognition is a principal factor in ensuring the safety of driving systems. This paper presents a novel system for learning and predicting driver motions and an event-based high-resolution (1280x720) dataset, N-DriverMotion, newly collected to train on a neuromorphic vision system. The system comprises an event-based camera…

21 Nov

Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods

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[Submitted on 18 Nov 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods, by Jai Doshi and Asa Cooper Stickland View PDF HTML (experimental) Abstract:Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods for LLM unlearning, achieving impressive results on unlearning benchmarks. We study in detail the efficacy of these methods by evaluating their impact on general model capabilities on…

21 Nov

Select High-Level Features: Efficient Experts from a Hierarchical Classification Network

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[Submitted on 8 Mar 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Select High-Level Features: Efficient Experts from a Hierarchical Classification Network, by Andr'e Kelm and 7 other authors View PDF HTML (experimental) Abstract:This study introduces a novel expert generation method that dynamically reduces task and computational complexity without compromising predictive performance. It is based on a new hierarchical classification network topology that combines sequential processing of generic low-level features with parallelism and nesting of high-level features. This structure allows for the innovative extraction technique: the ability to select only high-level…

21 Nov

DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes

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arXiv:2411.11921v1 Announce Type: new Abstract: We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios. Our approach employs a two-stage optimization pipeline of dynamic street Gaussians. In the first stage, we extract 2D motion masks based on the observation that 3D Gaussian Splatting inherently can reconstruct only the static regions in dynamic environments. These extracted 2D motion priors are then mapped into the Gaussian space in a differentiable manner, leveraging an efficient formulation of dynamic Gaussians in the second stage. Combined with the introduced geometric regularizations, our method are able…

21 Nov

HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives

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21 Nov

KX Aims to Unlock Python’s Potential with PyKX 3.0

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(dTosh/Shutterstock) As AI-driven algorithms become increasingly complex, the demand for scalable solutions that integrate powerful analytical engines with machine learning libraries has surged. KX, a performance analytical database for AI, has responded to this need by enhancing PyKX, its Python-first interface for kdb+, with a new hybrid architecture. PyKX 3.0 merges kdb+’s processing power with Python’s ML capabilities. The company claims that developers can use the platform to build advanced AI-driven applications and analytics without compromising on speed or scalability. In May 2023, KX had open-sourced PyKX, making its kdb+ time-series database and q programming language accessible to the global…

21 Nov

Refusal in LLMs is an Affine Function

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[Submitted on 13 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Refusal in LLMs is an Affine Function, by Thomas Marshall and 2 other authors View PDF HTML (experimental) Abstract:We propose affine concept editing (ACE) as an approach for steering language models' behavior by intervening directly in activations. We begin with an affine decomposition of model activation vectors and show that prior methods for steering model behavior correspond to subsets of terms of this decomposition. We then provide a derivation of ACE and use it to control refusal behavior on…

21 Nov

Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

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[Submitted on 17 Jun 2024 (v1), last revised 19 Nov 2024 (this version, v3)] View a PDF of the paper titled Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities, by Felix Wagner and 8 other authors View PDF HTML (experimental) Abstract:Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a…