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15 Nov

SASE: A Searching Architecture for Squeeze and Excitation Operations

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arXiv:2411.08333v1 Announce Type: new Abstract: In the past few years, channel-wise and spatial-wise attention blocks have been widely adopted as supplementary modules in deep neural networks, enhancing network representational abilities while introducing low complexity. Most attention modules follow a squeeze-and-excitation paradigm. However, to design such attention modules, requires a substantial amount of experiments and computational resources. Neural Architecture Search (NAS), meanwhile, is able to automate the design of neural networks and spares the numerous experiments required for an optimal architecture. This motivates us to design a search architecture that can automatically find near-optimal attention modules through NAS. We propose SASE,…

15 Nov

Neural Topic Modeling with Large Language Models in the Loop

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15 Nov

TowerDebias: A Novel Debiasing Method based on the Tower Property

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arXiv:2411.08297v1 Announce Type: new Abstract: Decision-making processes have increasingly come to rely on sophisticated machine learning tools, raising concerns about the fairness of their predictions with respect to any sensitive groups. The widespread use of commercial black-box machine learning models necessitates careful consideration of their legal and ethical implications on consumers. In situations where users have access to these "black-box" models, a key question emerges: how can we mitigate or eliminate the influence of sensitive attributes, such as race or gender? We propose towerDebias (tDB), a novel approach designed to reduce the influence of sensitive variables in predictions made by…

15 Nov

ASTM :Autonomous Smart Traffic Management System Using Artificial Intelligence CNN and LSTM

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[Submitted on 14 Oct 2024 (v1), last revised 13 Nov 2024 (this version, v4)] View a PDF of the paper titled ASTM :Autonomous Smart Traffic Management System Using Artificial Intelligence CNN and LSTM, by Christofel Rio Goenawan View PDF HTML (experimental) Abstract:In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to…

15 Nov

Target-driven Attack for Large Language Models

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[Submitted on 9 Nov 2024 (v1), last revised 13 Nov 2024 (this version, v2)] View a PDF of the paper titled Target-driven Attack for Large Language Models, by Chong Zhang and 5 other authors View PDF HTML (experimental) Abstract:Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there is currently a large amount of research on black-box attacks, most of these black-box attacks use random…

15 Nov

Interaction Testing in Variation Analysis

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arXiv:2411.08861v1 Announce Type: cross Abstract: Relationships of cause and effect are of prime importance for explaining scientific phenomena. Often, rather than just understanding the effects of causes, researchers also wish to understand how a cause $X$ affects an outcome $Y$ mechanistically -- i.e., what are the causal pathways that are activated between $X$ and $Y$. For analyzing such questions, a range of methods has been developed over decades under the rubric of causal mediation analysis. Traditional mediation analysis focuses on decomposing the average treatment effect (ATE) into direct and indirect effects, and therefore focuses on the ATE as the central…

15 Nov

Motion Control for Enhanced Complex Action Video Generation

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arXiv:2411.08328v1 Announce Type: new Abstract: Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions. A key limitation lies in the text prompt's inability to precisely convey intricate motion details. To address this, we propose a novel framework, MVideo, designed to produce long-duration videos with precise, fluid actions. MVideo overcomes the limitations of text prompts by incorporating mask sequences as an additional motion condition input, providing a clearer, more accurate representation of intended actions. Leveraging foundational vision models such as GroundingDINO and SAM2, MVideo automatically generates mask sequences, enhancing both efficiency and robustness. Our results…

15 Nov

Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

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arXiv:2411.08516v1 Announce Type: new Abstract: The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Despite the impressive capabilities of large language models (LLMs) in advancing the natural language understanding frontier, their application to large-scale tabular data presents significant challenges, specifically regarding table size and complex intricate relationships. Existing works have shown promise with small-scale tables but often flounder when tasked with the complex reasoning required by larger, interconnected tables found in real-world scenarios. To address this gap, we introduce "Tree-of-Table", a novel approach designed to…

15 Nov

Hashing for Protein Structure Similarity Search

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arXiv:2411.08286v1 Announce Type: new Abstract: Protein structure similarity search (PSSS), which tries to search proteins with similar structures, plays a crucial role across diverse domains from drug design to protein function prediction and molecular evolution. Traditional alignment-based PSSS methods, which directly calculate alignment on the protein structures, are highly time-consuming with high memory cost. Recently, alignment-free methods, which represent protein structures as fixed-length real-valued vectors, are proposed for PSSS. Although these methods have lower time and memory cost than alignment-based methods, their time and memory cost is still too high for large-scale PSSS, and their accuracy is unsatisfactory. In this…

15 Nov

Scaling Properties of Diffusion Models for Perceptual Tasks

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[Submitted on 12 Nov 2024 (v1), last revised 13 Nov 2024 (this version, v2)] View a PDF of the paper titled Scaling Properties of Diffusion Models for Perceptual Tasks, by Rahul Ravishankar and 3 other authors View PDF HTML (experimental) Abstract:In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a…