Researchers from OpenAI, Anthropic, and Google DeepMind found that adaptive attacks bypassed 12 AI defenses that claimed near ...
Abstract: Accurate translational compensation is crucial for high-resolution inverse synthetic aperture radar imaging. The parameterized optimization methods can directly compensate for the ...
Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Trump pulls US out of more than 30 UN bodies ICE shooting ...
Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns and to ...
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
In autonomous driving, understanding the 3D world over time is critical. Yet, most vision-based 3D Occupancy (VisionOcc) methods only scratch the surface of temporal fusion, focusing on simple ...
A search problem refers to the task of finding a solution within some space of possible options, and that space could be made up of discrete steps or continuously varying values. For example, solving ...
Abstract: This letter proposes a continuous-time heavy-ball dynamical system to solve a constrained nonlinear optimization problem, ensuring forward invariance of the constraint set. Recent research ...
ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...
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