111 citations · 261 across the 22 of their papers we have counts for
22 papers
MobileQuant: Mobile-friendly Quantization for On-device Language Models
Fuwen Tan, Royson Lee, Łukasz Dudziak +5
Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. However, deploying LLMs on edge devices poses sev…
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Bingchen Zhao, Yongshuo Zong, Letian Zhang +1
The advancement of large language models (LLMs) has significantly broadened the scope of applications in natural language processing, with multi-modal LLMs extending these capabili…
SketchINR: A First Look into Sketches as Implicit Neural Representations
Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +4
We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimen…
Sketch-based Video Object Segmentation: Benchmark and Analysis
Ruolin Yang, Da Li, Conghui Hu +3
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a la…
FedL2P: Federated Learning to Personalize
Royson Lee, Minyoung Kim, Da Li +4
Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those comm…
BayesDLL: Bayesian Deep Learning Library
Minyoung Kim, Timothy Hospedales
We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream approximate Bayesian inference algorithms: variational…