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Found 7 papers, 2 papers with code
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
In this paper, we propose FALCON, a novel combinatorial-optimization-based framework for network pruning that jointly takes into account model accuracy (fidelity), FLOPs, and sparsity constraints.
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey
This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose estimation, pose tracking, and action recognition. Pose estimation involves the determination of human joint positions from images or image sequences.
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Our approach, CHITA, extends the classical Optimal Brain Surgeon framework and results in significant improvements in speed, memory, and performance over existing optimization-based approaches for network pruning.
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Recently there has been significant interest in learning optimal decision trees using various approaches (e. g., based on integer programming, dynamic programming) -- to achieve computational scalability, most of these approaches focus on classification tasks with binary features.
Efficient phase-factor evaluation in quantum signal processing
Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers.
Quantum Physics Optimization and Control Computational Physics
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items.
Dynamic Mean-Variance Portfolio Optimisation
The portfolio optimisation problem, first raised by Harry Markowitz in 1952, has been a fundamental and central topic to understanding the stock market and making decisions.
