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Found 8 papers, 3 papers with code
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
While compared in terms of runtime, AFLoRA can yield up to $1. 86\times$ improvement as opposed to similar PEFT alternatives.
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity.
Induced Generative Adversarial Particle Transformers
In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC).
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN).
Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions
This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict.
Maximizing Safety and Efficiency for Cooperative Lane-Changing: A Minimally Disruptive Approach
This paper addresses cooperative lane-changing maneuvers in mixed traffic, aiming to minimize traffic flow disruptions while accounting for uncooperative vehicles.
Cooperative Lane Changing in Mixed Traffic can be Robust to Human Driver Behavior
We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic.
Evaluating generative models in high energy physics
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).
Generative Adversarial Network
Vocal Bursts Intensity Prediction
