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Collections including paper arxiv:2202.01169

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs

Paper • 2509.06550 • Published Sep 8, 2025 • 2
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection

Paper • 2310.10661 • Published Sep 14, 2023 • 1
Prime Collective Communications Library -- Technical Report

Paper • 2505.14065 • Published May 20, 2025
A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation

Paper • 2508.01047 • Published Aug 1, 2025

Papers - MoE - Router - Research

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts

Paper • 2306.04845 • Published Jun 8, 2023 • 4
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

Paper • 2306.04073 • Published Jun 7, 2023 • 2
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Paper • 2403.07816 • Published Mar 12, 2024 • 44
Unified Scaling Laws for Routed Language Models

Paper • 2202.01169 • Published Feb 2, 2022 • 2

Papers - MoE - Training

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Paper • 2308.10110 • Published Aug 19, 2023 • 2
Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
ConstitutionalExperts: Training a Mixture of Principle-based Prompts

Paper • 2403.04894 • Published Mar 7, 2024 • 2
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Paper • 2403.03432 • Published Mar 6, 2024 • 1

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

Paper • 2009.10622 • Published Sep 22, 2020 • 1
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 53
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8, 2024 • 74
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

Paper • 2401.14361 • Published Jan 25, 2024 • 2

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 7
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 10
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Papers - Model Scaling - Effective Parameter Count

Unified Scaling Laws for Routed Language Models

Paper • 2202.01169 • Published Feb 2, 2022 • 2

Papers - MoE - Router

Turn Waste into Worth: Rectifying Top-k Router of MoE

Paper • 2402.12399 • Published Feb 17, 2024 • 2
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition

Paper • 2402.02526 • Published Feb 4, 2024 • 3
Buffer Overflow in Mixture of Experts

Paper • 2402.05526 • Published Feb 8, 2024 • 9
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29, 2024 • 28

Papers - MoE - Research

Adaptive sequential Monte Carlo by means of mixture of experts

Paper • 1108.2836 • Published Aug 14, 2011 • 2
Convergence Rates for Mixture-of-Experts

Paper • 1110.2058 • Published Oct 10, 2011 • 2
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Paper • 2310.12008 • Published Oct 18, 2023 • 2
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

Paper • 2308.11793 • Published Aug 22, 2023 • 2

Papers - Training Research

Measuring the Effects of Data Parallelism on Neural Network Training

Paper • 1811.03600 • Published Nov 8, 2018 • 2
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Paper • 1804.04235 • Published Apr 11, 2018 • 2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 3
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 65

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 7
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 10
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs

Paper • 2509.06550 • Published Sep 8, 2025 • 2
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection

Paper • 2310.10661 • Published Sep 14, 2023 • 1
Prime Collective Communications Library -- Technical Report

Paper • 2505.14065 • Published May 20, 2025
A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation

Paper • 2508.01047 • Published Aug 1, 2025

Papers - Model Scaling - Effective Parameter Count

Unified Scaling Laws for Routed Language Models

Paper • 2202.01169 • Published Feb 2, 2022 • 2

Papers - MoE - Router - Research

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts

Paper • 2306.04845 • Published Jun 8, 2023 • 4
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

Paper • 2306.04073 • Published Jun 7, 2023 • 2
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Paper • 2403.07816 • Published Mar 12, 2024 • 44
Unified Scaling Laws for Routed Language Models

Paper • 2202.01169 • Published Feb 2, 2022 • 2

Papers - MoE - Router

Turn Waste into Worth: Rectifying Top-k Router of MoE

Paper • 2402.12399 • Published Feb 17, 2024 • 2
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition

Paper • 2402.02526 • Published Feb 4, 2024 • 3
Buffer Overflow in Mixture of Experts

Paper • 2402.05526 • Published Feb 8, 2024 • 9
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29, 2024 • 28

Papers - MoE - Training

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Paper • 2308.10110 • Published Aug 19, 2023 • 2
Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
ConstitutionalExperts: Training a Mixture of Principle-based Prompts

Paper • 2403.04894 • Published Mar 7, 2024 • 2
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Paper • 2403.03432 • Published Mar 6, 2024 • 1

Papers - MoE - Research

Adaptive sequential Monte Carlo by means of mixture of experts

Paper • 1108.2836 • Published Aug 14, 2011 • 2
Convergence Rates for Mixture-of-Experts

Paper • 1110.2058 • Published Oct 10, 2011 • 2
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Paper • 2310.12008 • Published Oct 18, 2023 • 2
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

Paper • 2308.11793 • Published Aug 22, 2023 • 2

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

Paper • 2009.10622 • Published Sep 22, 2020 • 1
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 53
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8, 2024 • 74
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

Paper • 2401.14361 • Published Jan 25, 2024 • 2

Papers - Training Research

Measuring the Effects of Data Parallelism on Neural Network Training

Paper • 1811.03600 • Published Nov 8, 2018 • 2
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Paper • 1804.04235 • Published Apr 11, 2018 • 2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 3
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 65

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 7
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 10
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 7
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 10
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

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