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ayBKRjGDEI
|
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
|
data/openreview_paper/ICML_2023_oral/ayBKRjGDEI//paper.pdf
| 61
| 27
|
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nS2x7LOKZk
|
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.
|
data/openreview_paper/ICML_2023_oral/nS2x7LOKZk//paper.pdf
| 44
| 0
|
[] |
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] |
[] |
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Ovu1horBiZ
|
Reinforcement Learning from Passive Data via Latent Intentions
|
data/openreview_paper/ICML_2023_oral/Ovu1horBiZ//paper.pdf
| 38
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jwy77xkyPt
|
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
|
data/openreview_paper/ICML_2023_oral/jwy77xkyPt//paper.pdf
| 54
| 23
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CXkJh2ITml
|
Bayes-optimal Learning of Deep Random Networks of Extensive-width
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data/openreview_paper/ICML_2023_oral/CXkJh2ITml//paper.pdf
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O1j4uFuSVW
|
Adapting to game trees in zero-sum imperfect information games
|
data/openreview_paper/ICML_2023_oral/O1j4uFuSVW//paper.pdf
| 57
| 27
|
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qlAtMW9jIh
|
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
|
data/openreview_paper/ICML_2023_oral/qlAtMW9jIh//paper.pdf
| 44
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XAK3238obr
|
How Bad is Top-$K$ Recommendation under Competing Content Creators?
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data/openreview_paper/ICML_2023_oral/XAK3238obr//paper.pdf
| 41
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6rlGbYv4bT
|
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
|
data/openreview_paper/ICML_2023_oral/6rlGbYv4bT//paper.pdf
| 32
| 0
|
[] |
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DH11pt7S2t
|
Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
|
data/openreview_paper/ICML_2023_oral/DH11pt7S2t//paper.pdf
| 51
| 0
|
[] |
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Lhyy8H75KA
|
Scaling Vision Transformers to 22 Billion Parameters
|
data/openreview_paper/ICML_2023_oral/Lhyy8H75KA//paper.pdf
| 123
| 0
|
[] |
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] |
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wLAMOoL0KD
|
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
|
data/openreview_paper/ICML_2023_oral/wLAMOoL0KD//paper.pdf
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[
{
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"title": "Training deep nets with sublinear memory cost",
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"B Kaul",
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"title": "Distributed deep learning using synchronous stochastic gradient descent",
"year": "2016"
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{
"authors": [
"Y Huang",
"Y Cheng",
"A Bapna",
"O Firat",
"D Chen",
"M Chen",
"H Lee",
"J Ngiam",
"Q V Le",
"Y Wu"
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"ref_id": "b10",
"title": "Gpipe: Efficient training of giant neural networks using pipeline parallelism",
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{
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"ref_id": "b11",
"title": "Contrastive Code Representation Learning",
"year": "2020"
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{
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"J Brennan",
"M He",
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"T Chen",
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"title": "Dynamic tensor rematerialization",
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"title": "A graph theoretic framework of recomputation algorithms for memory-efficient backpropagation",
"year": "2019"
},
{
"authors": [
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"Haruki Imai",
"Yasushi Negishi",
"Kiyokuni Kawachiya"
],
"doi": "10.1145/3315573.3329984",
"ref_id": "b15",
"title": "Automatic GPU memory management for large neural models in TensorFlow",
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{
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"Hailong Yang",
"Qingxiao Sun",
"Biao Sun",
"Jiwei Hao",
"Tianyu Feng",
"Fengwei Yu",
"Shengdong Chen",
"Ye Tao",
"Zicheng Zhang",
"Zhongzhi Luan",
"Depei Qian"
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"doi": "10.1109/ipdps54959.2023.00025",
"ref_id": "b16",
"title": "Exploiting Input Tensor Dynamics in Activation Checkpointing for Efficient Training on GPU",
"year": "2022"
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{
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"Deepak Narayanan",
"Aaron Harlap",
"Amar Phanishayee",
"Vivek Seshadri",
"Nikhil R Devanur",
"Gregory R Ganger",
"Phillip B Gibbons",
"Matei Zaharia"
],
"doi": "10.1145/3341301.3359646",
"ref_id": "b17",
"title": "PipeDream",
"year": "2019"
},
{
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"Uwe Naumann"
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"title": "Call Tree Reversal is NP-Complete",
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{
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"S G Patil",
"P Jain",
"P Dutta",
"I Stoica",
"J Gonzalez",
"Poet"
],
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"title": "Training neural networks on tiny devices with integrated rematerialization and paging",
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{
"authors": [
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"Jason Clemons",
"Arslan Zulfiqar",
"Stephen W Keckler"
],
"doi": "10.1109/micro.2016.7783721",
"ref_id": "b20",
"title": "vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design",
"year": "2016"
},
{
"authors": [
"B Steiner",
"M Elhoushi",
"J Kahn",
"J Hegarty",
"Olla"
],
"doi": "",
"ref_id": "b21",
"title": "Optimizing the lifetime and location of arrays to reduce the memory usage of neural networks",
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