ICML 2024PastEfficiencyOptimization
2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)
WANT@ICML 2024
- Submission deadline
- Jun 3, 2024, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (42)
Fetched from OpenReview (v2) on 2026-06-10.
Accelerating Best-of-N via Speculative Rejection
AdaMeM: Memory Efficient Momentum for Adafactor
Adaptive Model Pruning in Federated Learning through Loss Exploration
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
An Analytical Approach to Enhancing DNN Efficiency and Accuracy Using Approximate Multiplication
Asynchronous Local-SGD Training for Language Modeling
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates
Boolean Logic for Low-Energy Deep Learning
Can LLMs Enhance Performance Prediction for Deep Learning Models?
Class-aware Initialization of Early Exits for Pre-training Large Language Models
Coarse-to-Fine Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition
Communication Efficient Federated Learning with Differentiated Aggregation
DASH: Warm-Starting Neural Network Training Without Loss of Plasticity Under Stationarity
DiLoCo: Distributed Low-Communication Training of Language Models
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX
ECO: Efficient Computational Optimization for Exact Machine Unlearning in Deep Neural Networks
Effective Layer Pruning Through Similarity Metric Perspective
Efficient Adaptive Federated Optimization
Efficient Document Ranking with Learnable Late Interactions
Enhancing Fine-grained Multi-modal Alignment via Adapters: A Parameter-Efficient Training Framework for Referring Image Segmentation
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives
Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough
Liouna: Biologically Plausible Learning for Efficient Pre-Training of Transferrable Deep Models
LoQT: Low Rank Adapters for Quantized Training
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
Lowering PyTorch's Memory Consumption for Selective Differentiation
Memory and Bandwidth are All You Need for Fully Sharded Data Parallel
Model-Agnostic Graph Dataset Compression with the Tree Mover’s Distance
MoReDrop: Dropout without Dropping
Multi-objective Differentiable Neural Architecture Search
Optimistic Asynchrony Control: Achieving Synchronous Convergence With Asynchronous Throughput for Embedding Model Training
Resolving Discrepancies in Compute-Optimal Scaling of Language Models
Resource-constrained Neural Architecture Search on Language Models: A Case Study
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models
Scalify: scale propagation for efficient low-precision LLM training
Single Train Multi Deploy on Topology Search Spaces using Kshot-Hypernet
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Towards Efficient and Scalable Training of Differentially Private Deep Learning
u-μP: The Unit-Scaled Maximal Update Parametrization
Variational Stochastic Gradient Descent for Deep Neural Networks
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity