5 citations · 5 across the 7 of their papers we have counts for
7 papers
Advancing Multi-Instrument Music Transcription: Results from the 2025 AMT Challenge
Ojas Chaturvedi, Kayshav Bhardwaj, Tanay Gondil +5
This paper presents the results of the 2025 Automatic Music Transcription (AMT) Challenge, an online competition to benchmark progress in multi-instrument transcription. Eight team…
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…
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…
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…
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
Purvish Jajal, Wenxin Jiang, Arav Tewari +6
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models betw…