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M. Das

7 papers here

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

author position
  • first author4
  • middle author2

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI3
same name
  • M. Das — 29 papers, h 16
  • M. Das — 14 papers, h 18
  • M. Das — 14 papers, h 12
  • M. Das — 12 papers, h 20
  • M. Das — 5 papers, h 14
  • M. Das — 4 papers, h 10

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
20182022
most citedUser Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams

8 citations · 8 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022

Tree DNN: A Deep Container Network

Brijraj Singh, Swati Gupta, Mayukh Das +2

Multi-Task Learning (MTL) has shown its importance at user products for fast training, data efficiency, reduced overfitting etc. MTL achieves it by sharing the network parameters a…

cs.LG2022

AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement

Mayukh Das, Brijraj Singh, Harsh Kanti Chheda +2

Designing suitable deep model architectures, for AI-driven on-device apps and features, at par with rapidly evolving mobile hardware and increasingly complex target scenarios is a…

cs.LG2019

Knowledge-augmented Column Networks: Guiding Deep Learning with Advice

Mayukh Das, Devendra Singh Dhami, Yang Yu +2

Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major ch…

cs.LG2019

Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice

Mayukh Das, Yang Yu, Devendra Singh Dhami +2

Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with…

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