3 citations · 4 across the 10 of their papers we have counts for
12 papers
On a Separate Note: Robust Score-Informed Note Separation with a Two-Stream TFC-TDF U-Net and Adaptive Set Ownership
Benjamin Shiue-Hal Chou, Purvish Jajal, Nicholas John Eliopoulos +5
Score-informed note separation seeks to extract the performed waveform of all individual notes, often from a polyphonic recording. Existing deep learning systems generally only tar…
AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens
Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou +3
Modern transformer architectures achieve remarkable performance across tasks and domains but remain rigid in how they allocate computation at inference time. Real-world deployment…
LadderSym: A Multimodal Interleaved Transformer for Music Practice Error Detection
Benjamin Shiue-Hal Chou, Purvish Jajal, Nick John Eliopoulos +4
Music learners can greatly benefit from tools that accurately detect errors in their practice. Existing approaches typically compare audio recordings to music scores using heuristi…
SysLLMatic: Large Language Models are Software System Optimizers
Huiyun Peng, Arjun Gupte, Ryan Hasler +7
Automatic software system optimization can improve software speed, reduce operating costs, and save energy. Traditional approaches to optimization rely on manual tuning and compile…
Inference-Time Alignment of Diffusion Models via Evolutionary Algorithms
Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou +3
Diffusion models are state-of-the-art generative models, yet their samples often fail to satisfy application objectives such as safety constraints or domain-specific validity. Exis…
Detecting Music Performance Errors with Transformers
Benjamin Shiue-Hal Chou, Purvish Jajal, Nicholas John Eliopoulos +6
Beginner musicians often struggle to identify specific errors in their performances, such as playing incorrect notes or rhythms. There are two limitations in existing tools for mus…
Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
Huiyun Peng, Arjun Gupte, Nicholas John Eliopoulos +8
Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet o…
Token Turing Machines are Efficient Vision Models
Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou +3
We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines and Token Turin…
Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge
Nick John Eliopoulos, Purvish Jajal, James C. Davis +3
This paper investigates how to efficiently deploy vision transformers on edge devices for small workloads. Recent methods reduce the latency of transformer neural networks by remov…
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Caleb Tung, Nicholas Eliopoulos, Purvish Jajal +7
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requireme…
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Caleb Tung, Abhinav Goel, Fischer Bordwell +4
Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors are not perfect. On two images that look similar to human ey…
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
Caleb Tung, Abhinav Goel, Xiao Hu +5
Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and I…