Bilge Acun

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it
• Computer Vision and Pattern Recognition — 90 citations into it
• Information Systems — 67 citations into it
• ML Systems & Hardware — 45 citations into it
• Recommender Systems — 21 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Bilge Acun trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Udit Gupta
2. Maxim Naumov
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Carole-Jean Wu
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Yoshua Bengio
10. Paul Covington

Who trusts Bilge Acun overall

1. Carole-Jean Wu
2. Geet Sethi
3. Dheevatsa Mudigere

Papers from Bilge Acun's perspective

Ordered by how strongly the researchers Bilge Acun trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021) — 662 citations
• LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding (2024) — 296 citations
• RecShard: statistical feature-based memory optimization for industry-scale neural recommendation (2022) — 81 citations
• A Holistic Approach for Designing Carbon Aware Datacenters (2022) — 31 citations
• Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads (2021) — 22 citations
• TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models (2021) — 122 citations
• Understanding Training Efficiency of Deep Learning Recommendation Models at Scale (2020) — 128 citations