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ORCIDDan Alistarh
Person information
- affiliation: IST Austria, Klosterneuburg, Austria
- affiliation (former): MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA
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2020 – today
- 2023
[j32]Vitaly Aksenov
, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami:
The splay-list: a distribution-adaptive concurrent skip-list. Distributed Comput. 36(3): 395-418 (2023)- [j31]Nikita Koval, Dmitry Khalanskiy, Dan Alistarh:
CQS: A Formally-Verified Framework for Fair and Abortable Synchronization. Proc. ACM Program. Lang. 7(PLDI): 244-266 (2023) - [j30]Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu:
Why Extension-Based Proofs Fail. SIAM J. Comput. 52(4): 913-944 (2023) - [j29]Dan Alistarh:
Distributed Computing Column 86: A Summary of PODC 2022. SIGACT News 54(1): 105 (2023) - [j28]Dan Alistarh, Alkida Balliu, Dimitrios Los, Sean Ovens:
A Brief Summary of PODC 2022. SIGACT News 54(1): 106-112 (2023) - [j27]Dan Alistarh, Faith Ellen, Joel Rybicki:
Wait-free approximate agreement on graphs. Theor. Comput. Sci. 948: 113733 (2023) - [c107]Nikita Koval, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, Dan Alistarh:
Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM. CAV (1) 2023: 156-169 - [c106]Eugenia Iofinova, Alexandra Peste, Dan Alistarh:
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CVPR 2023: 24364-24373 - [c105]Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh:
OPTQ: Accurate Quantization for Generative Pre-trained Transformers. ICLR 2023 - [c104]Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh:
CrAM: A Compression-Aware Minimizer. ICLR 2023 - [c103]Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. ICML 2023: 10323-10337 - [c102]Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh:
Quantized Distributed Training of Large Models with Convergence Guarantees. ICML 2023: 24020-24044 - [c101]Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh:
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge. ICML 2023: 26215-26227 - [c100]Nikita Koval, Dan Alistarh, Roman Elizarov:
Fast and Scalable Channels in Kotlin Coroutines. PPoPP 2023: 107-118 - [c99]Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze, Dan Alistarh:
Provably-Efficient and Internally-Deterministic Parallel Union-Find. SPAA 2023: 261-271 - [i89]Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot. CoRR abs/2301.00774 (2023) - [i88]Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh:
Quantized Distributed Training of Large Models with Convergence Guarantees. CoRR abs/2302.02390 (2023) - [i87]Eldar Kurtic, Elias Frantar, Dan Alistarh:
ZipLM: Hardware-Aware Structured Pruning of Language Models. CoRR abs/2302.04089 (2023) - [i86]Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh:
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks. CoRR abs/2302.04852 (2023) - [i85]Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh:
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. CoRR abs/2303.14409 (2023) - [i84]Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze, Dan Alistarh:
Provably-Efficient and Internally-Deterministic Parallel Union-Find. CoRR abs/2304.09331 (2023) - [i83]Eugenia Iofinova, Alexandra Peste, Dan Alistarh:
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CoRR abs/2304.12622 (2023) - [i82]Mher Safaryan, Alexandra Peste, Dan Alistarh:
Knowledge Distillation Performs Partial Variance Reduction. CoRR abs/2305.17581 (2023) - [i81]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR abs/2306.03078 (2023) - [i80]Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Dan Alistarh:
Error Feedback Can Accurately Compress Preconditioners. CoRR abs/2306.06098 (2023) - [i79]John Lazarsfeld, Dan Alistarh:
Decentralized Learning Dynamics in the Gossip Model. CoRR abs/2306.08670 (2023) - [i78]Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel:
QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. CoRR abs/2307.03738 (2023) - [i77]Dan Alistarh, Krishnendu Chatterjee, Mehrdad Karrabi, John Lazarsfeld:
Repeated Game Dynamics in Population Protocols. CoRR abs/2307.07297 (2023) - [i76]Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. CoRR abs/2308.02060 (2023) - [i75]Elias Frantar, Carlos Riquelme, Neil Houlsby, Dan Alistarh, Utku Evci:
Scaling Laws for Sparsely-Connected Foundation Models. CoRR abs/2309.08520 (2023) - 2022
- [j26]Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour:
Dynamic Averaging Load Balancing on Cycles. Algorithmica 84(4): 1007-1029 (2022) - [j25]Dan Alistarh:
Distributed Computing Column 85 Elastic Consistency: A Consistency Criterion for Distributed Optimization. SIGACT News 53(2): 63 (2022) - [j24]Dan Alistarh, Ilia Markov, Giorgi Nadiradze:
Elastic Consistency: A Consistency Criterion for Distributed Optimization. SIGACT News 53(2): 64-82 (2022) - [c98]Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh:
How Well Do Sparse ImageNet Models Transfer? CVPR 2022: 12256-12266 - [c97]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. EMNLP 2022: 4163-4181 - [c96]Elias Frantar, Dan Alistarh:
SPDY: Accurate Pruning with Speedup Guarantees. ICML 2022: 6726-6743 - [c95]Ilia Markov, Hamidreza Ramezani-Kebrya, Dan Alistarh:
CGX: adaptive system support for communication-efficient deep learning. Middleware 2022: 241-254 - [c94]Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. NeurIPS 2022 - [c93]Dan Alistarh, Joel Rybicki, Sasha Voitovych:
Near-Optimal Leader Election in Population Protocols on Graphs. PODC 2022: 246-256 - [c92]Anastasiia Postnikova, Nikita Koval, Giorgi Nadiradze, Dan Alistarh:
Multi-queues can be state-of-the-art priority schedulers. PPoPP 2022: 353-367 - [c91]Trevor Brown, William Sigouin, Dan Alistarh:
PathCAS: an efficient middle ground for concurrent search data structures. PPoPP 2022: 385-399 - [i74]Elias Frantar, Dan Alistarh:
SPDY: Accurate Pruning with Speedup Guarantees. CoRR abs/2201.13096 (2022) - [i73]Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh:
Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. CoRR abs/2203.06638 (2022) - [i72]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. CoRR abs/2203.07259 (2022) - [i71]Dan Alistarh, Joel Rybicki, Sasha Voitovych:
Near-Optimal Leader Election in Population Protocols on Graphs. CoRR abs/2205.12597 (2022) - [i70]Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh:
QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient. CoRR abs/2206.10032 (2022) - [i69]Alexandra Peste, Adrian Vladu, Dan Alistarh, Christoph H. Lampert:
CrAM: A Compression-Aware Minimizer. CoRR abs/2207.14200 (2022) - [i68]Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. CoRR abs/2208.11580 (2022) - [i67]Eldar Kurtic, Dan Alistarh:
GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods. CoRR abs/2210.06384 (2022) - [i66]Shayan Talaei, Giorgi Nadiradze, Dan Alistarh:
Hybrid Decentralized Optimization: First- and Zeroth-Order Optimizers Can Be Jointly Leveraged For Faster Convergence. CoRR abs/2210.07703 (2022) - [i65]Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh:
oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. CoRR abs/2210.09223 (2022) - [i64]Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh:
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. CoRR abs/2210.17323 (2022) - [i63]Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar, Dan Alistarh:
L-GreCo: An Efficient and General Framework for Layerwise-Adaptive Gradient Compression. CoRR abs/2210.17357 (2022) - [i62]Nikita Koval, Dan Alistarh, Roman Elizarov:
Fast and Scalable Channels in Kotlin Coroutines. CoRR abs/2211.04986 (2022) - [i61]Trevor Brown, William Sigouin, Dan Alistarh:
PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures. CoRR abs/2212.09851 (2022) - 2021
- [j23]Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy:
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization. J. Mach. Learn. Res. 22: 114:1-114:43 (2021) - [j22]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22: 241:1-241:124 (2021) - [j21]Dan Alistarh:
Distributed Computing Column 81: Byzantine Agreement with Less Communication: Recent Advances. SIGACT News 52(1): 70 (2021) - [j20]Dan Alistarh:
Distributed Computing Column 82 Distributed Computability: A Few Results Masters Students Should Know. SIGACT News 52(2): 91 (2021) - [j19]Dan Alistarh:
Distributed Computing Column 83 Five Ways Not To Fool Yourself: Designing Experiments for Understanding Performance. SIGACT News 52(3): 60 (2021) - [j18]Dan Alistarh:
Distributed Computing Column 84: Perspectives on the Paper "CCS Expressions, Finite State Processes, and Three Problems of Equivalence". SIGACT News 52(4): 74-75 (2021) - [j17]Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. IEEE Trans. Parallel Distributed Syst. 32(7): 1725-1739 (2021) - [c90]Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee, Dan Alistarh:
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. AAAI 2021: 8209-8216 - [c89]Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh:
Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. AAAI 2021: 9037-9045 - [c88]Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent. ICLR 2021 - [c87]Peter Davies, Vijaykrishna Gurunanthan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh:
New Bounds For Distributed Mean Estimation and Variance Reduction. ICLR 2021 - [c86]Foivos Alimisis, Peter Davies, Dan Alistarh:
Communication-Efficient Distributed Optimization with Quantized Preconditioners. ICML 2021: 196-206 - [c85]Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh:
Distributed Principal Component Analysis with Limited Communication. NeurIPS 2021: 2823-2834 - [c84]Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh:
Asynchronous Decentralized SGD with Quantized and Local Updates. NeurIPS 2021: 6829-6842 - [c83]Janne H. Korhonen, Dan Alistarh:
Towards Tight Communication Lower Bounds for Distributed Optimisation. NeurIPS 2021: 7254-7266 - [c82]Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh:
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. NeurIPS 2021: 8557-8570 - [c81]Elias Frantar, Eldar Kurtic, Dan Alistarh:
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. NeurIPS 2021: 14873-14886 - [c80]Dan Alistarh, Rati Gelashvili, Joel Rybicki:
Fast Graphical Population Protocols. OPODIS 2021: 14:1-14:18 - [c79]Dan Alistarh, Martin Töpfer, Przemyslaw Uznanski:
Comparison Dynamics in Population Protocols. PODC 2021: 55-65 - [c78]Dan Alistarh, Peter Davies:
Collecting Coupons is Faster with Friends. SIROCCO 2021: 3-12 - [c77]Dan Alistarh, Faith Ellen, Joel Rybicki:
Wait-Free Approximate Agreement on Graphs. SIROCCO 2021: 87-105 - [c76]Alexander Fedorov, Nikita Koval, Dan Alistarh:
A Scalable Concurrent Algorithm for Dynamic Connectivity. SPAA 2021: 208-220 - [c75]Dan Alistarh, Rati Gelashvili, Giorgi Nadiradze:
Lower Bounds for Shared-Memory Leader Election Under Bounded Write Contention. DISC 2021: 4:1-4:17 - [c74]Dan Alistarh, Rati Gelashvili, Joel Rybicki:
Brief Announcement: Fast Graphical Population Protocols. DISC 2021: 43:1-43:4 - [i60]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR abs/2102.00554 (2021) - [i59]Foivos Alimisis, Peter Davies, Dan Alistarh:
Communication-Efficient Distributed Optimization with Quantized Preconditioners. CoRR abs/2102.07214 (2021) - [i58]Dan Alistarh, Rati Gelashvili, Joel Rybicki:
Fast Graphical Population Protocols. CoRR abs/2102.08808 (2021) - [i57]Dan Alistarh, Faith Ellen, Joel Rybicki:
Wait-free approximate agreement on graphs. CoRR abs/2103.08949 (2021) - [i56]Alexander Fedorov, Nikita Koval, Dan Alistarh:
A Scalable Concurrent Algorithm for Dynamic Connectivity. CoRR abs/2105.08098 (2021) - [i55]Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh:
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. CoRR abs/2106.12379 (2021) - [i54]Elias Frantar, Eldar Kurtic, Dan Alistarh:
Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. CoRR abs/2107.03356 (2021) - [i53]Alexandra Peste, Dan Alistarh, Christoph H. Lampert:
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models. CoRR abs/2107.03860 (2021) - [i52]Dan Alistarh, Rati Gelashvili, Giorgi Nadiradze:
Lower Bounds for Shared-Memory Leader Election under Bounded Write Contention. CoRR abs/2108.02802 (2021) - [i51]Anastasiia Postnikova, Nikita Koval, Giorgi Nadiradze, Dan Alistarh:
Multi-Queues Can Be State-of-the-Art Priority Schedulers. CoRR abs/2109.00657 (2021) - [i50]Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh:
Distributed Principal Component Analysis with Limited Communication. CoRR abs/2110.14391 (2021) - [i49]Ilia Markov, Hamidreza Ramezani-Kebrya, Dan Alistarh:
Project CGX: Scalable Deep Learning on Commodity GPUs. CoRR abs/2111.08617 (2021) - [i48]Nikita Koval, Dmitry Khalanskiy, Dan Alistarh:
A Formally-Verified Framework for Fair Synchronization in Kotlin Coroutines. CoRR abs/2111.12682 (2021) - [i47]Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh:
How Well Do Sparse Imagenet Models Transfer? CoRR abs/2111.13445 (2021) - [i46]Dan Alistarh, Peter Davies:
Collecting Coupons is Faster with Friends. CoRR abs/2112.05830 (2021) - 2020
- [j16]Dan Alistarh:
Distributed Computing Column 77 Consensus Dynamics: An Overview. SIGACT News 51(1): 57 (2020) - [j15]Dan Alistarh:
Distributed Computing Column 78: 60 Years of Mastering Concurrent Computing through Sequential Thinking. SIGACT News 51(2): 58 (2020) - [j14]Dan Alistarh:
Distributed Computing Column 79: Using Round Elimination to Understand Locality. SIGACT News 51(3): 62 (2020) - [j13]Dan Alistarh:
Distributed Computing Column 80: Annual Review 2020. SIGACT News 51(4): 73-74 (2020) - [j12]Nezihe Merve Gürel, Kaan Kara, Alen Stojanov, Tyler M. Smith, Thomas Lemmin, Dan Alistarh, Markus Püschel, Ce Zhang:
Compressive Sensing Using Iterative Hard Thresholding With Low Precision Data Representation: Theory and Applications. IEEE Trans. Signal Process. 68: 4268-4282 (2020) - [c73]Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour:
Dynamic Averaging Load Balancing on Cycles. ICALP 2020: 7:1-7:16 - [c72]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML 2020: 5416-5425 - [c71]Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William M. Leiserson, Sage Moore, Nir Shavit, Dan Alistarh:
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. ICML 2020: 5533-5543 - [c70]Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen:
Scalable Belief Propagation via Relaxed Scheduling. NeurIPS 2020 - [c69]Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. NeurIPS 2020 - [c68]Sidak Pal Singh, Dan Alistarh:
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression. NeurIPS 2020 - [c67]Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu:
Brief Announcement: Why Extension-Based Proofs Fail. PODC 2020: 54-56 - [c66]Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler:
Taming unbalanced training workloads in deep learning with partial collective operations. PPoPP 2020: 45-61 - [c65]Trevor Brown, Aleksandar Prokopec, Dan Alistarh:
Non-blocking interpolation search trees with doubly-logarithmic running time. PPoPP 2020: 276-291 - [c64]Nikita Koval, Maria Sokolova, Alexander Fedorov, Dan Alistarh, Dmitry Tsitelov:
Testing concurrency on the JVM with lincheck. PPoPP 2020: 423-424 - [c63]Dan Alistarh, Trevor Brown, Nandini Singhal:
Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures. SPAA 2020: 37-49 - [c62]Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami:
The Splay-List: A Distribution-Adaptive Concurrent Skip-List. DISC 2020: 3:1-3:18 - [i45]Aleksandar Prokopec, Trevor Brown, Dan Alistarh:
Analysis and Evaluation of Non-Blocking Interpolation Search Trees. CoRR abs/2001.00413 (2020) - [i44]Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev:
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. CoRR abs/2001.05918 (2020) - [i43]Dan Alistarh, Saleh Ashkboos, Peter Davies:
Distributed Mean Estimation with Optimal Error Bounds. CoRR abs/2002.09268 (2020) - [i42]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR abs/2002.10384 (2020) - [i41]Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen:
Relaxed Scheduling for Scalable Belief Propagation. CoRR abs/2002.11505 (2020) - [i40]Dan Alistarh, Martin Töpfer, Przemyslaw Uznanski:
Robust Comparison in Population Protocols. CoRR abs/2003.06485 (2020) - [i39]Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour:
Dynamic Averaging Load Balancing on Cycles. CoRR abs/2003.09297 (2020) - [i38]Dan Alistarh, Nikita Koval, Giorgi Nadiradze:
Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers. CoRR abs/2003.09363 (2020) - [i37]Sidak Pal Singh, Dan Alistarh:
WoodFisher: Efficient second-order approximations for model compression. CoRR abs/2004.14340 (2020) - [i36]Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. CoRR abs/2005.00124 (2020) - [i35]