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Michael Jones

11 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.DC3
  • cs.AI2
  • cs.CL1
  • cs.DB1
ORCID 0000-0001-5215-2346
same name
  • Michael Jones — 26 papers, h 18
  • Michael Jones — 5 papers, h 6
  • Michael Jones — 4 papers, h 9
  • Michael Jones — 3 papers, h 11
  • Michael Jones — 2 papers
  • Michael Jones — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedFrom Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference

15 citations · 47 across the 11 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 1 cited

Multimodal 3D Object Detection on Unseen Domains

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2

LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and eva…

cs.CV2024

Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2

Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmen…

cs.CV2023

Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection

Manish Sharma, Moitreya Chatterjee, Kuan-Chuan Peng +2

The primary bottleneck towards obtaining good recognition performance in IR images is the lack of sufficient labeled training data, owing to the cost of acquiring such data. Realiz…

cs.CV2023

Pixel-Grounded Prototypical Part Networks

Zachariah Carmichael, Suhas Lohit, Anoop Cherian +2

Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machine learning. Their prototype learning sc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.