MoMo: Momentum Models for Adaptive Learning Rates
- URL: http://arxiv.org/abs/2305.07583v3
- Date: Wed, 5 Jun 2024 14:03:57 GMT
- Title: MoMo: Momentum Models for Adaptive Learning Rates
- Authors: Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower,
- Abstract summary: We develop new Polyak-type adaptive learning rates that can be used on top of any momentum method.
We first develop MoMo, a Momentum Model based adaptive learning rate for SGD-M.
We show how MoMo can be used in combination with any momentum-based method, and showcase this by developing MoMo-Adam.
- Score: 14.392926033512069
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Training a modern machine learning architecture on a new task requires extensive learning-rate tuning, which comes at a high computational cost. Here we develop new Polyak-type adaptive learning rates that can be used on top of any momentum method, and require less tuning to perform well. We first develop MoMo, a Momentum Model based adaptive learning rate for SGD-M (stochastic gradient descent with momentum). MoMo uses momentum estimates of the losses and gradients sampled at each iteration to build a model of the loss function. Our model makes use of any known lower bound of the loss function by using truncation, e.g. most losses are lower-bounded by zero. The model is then approximately minimized at each iteration to compute the next step. We show how MoMo can be used in combination with any momentum-based method, and showcase this by developing MoMo-Adam, which is Adam with our new model-based adaptive learning rate. We show that MoMo attains a $\mathcal{O}(1/\sqrt{K})$ convergence rate for convex problems with interpolation, needing knowledge of no problem-specific quantities other than the optimal value. Additionally, for losses with unknown lower bounds, we develop on-the-fly estimates of a lower bound, that are incorporated in our model. We show that MoMo and MoMo-Adam improve over SGD-M and Adam in terms of robustness to hyperparameter tuning for training image classifiers on MNIST, CIFAR, and Imagenet, for recommender systems on Criteo, for a transformer model on the translation task IWSLT14, and for a diffusion model.
Related papers
- LoRA Unlearns More and Retains More (Student Abstract) [0.0]
PruneLoRA reduces the need for large-scale parameter updates by applying low-rank updates to the model.
We leverage LoRA to selectively modify a subset of the pruned model's parameters, thereby reducing the computational cost, memory requirements and improving the model's ability to retain performance on the remaining classes.
arXiv Detail & Related papers (2024-11-16T16:47:57Z) - LaDiMo: Layer-wise Distillation Inspired MoEfier [1.6199400106794555]
We propose a novel algorithm, LaDiMo, which efficiently converts a Transformer-based non-MoE model into a MoE model with minimal additional training cost.
We demonstrate the effectiveness of our method by converting the LLaMA2-7B model to a MoE model using only 100K tokens.
arXiv Detail & Related papers (2024-08-08T07:37:26Z) - EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods.
EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
arXiv Detail & Related papers (2024-05-23T05:25:45Z) - Maximum Entropy Model Correction in Reinforcement Learning [29.577846986302518]
We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning.
We introduce the Model Correcting Value Iteration (MoCoVI) algorithm, and its sampled-based variant MoCoDyna.
Unlike traditional model-based algorithms, MoCoVI and MoCoDyna effectively utilize an approximate model and still converge to the correct value function.
arXiv Detail & Related papers (2023-11-29T18:00:41Z) - Noise-in, Bias-out: Balanced and Real-time MoCap Solving [13.897997236684283]
We apply machine learning to solve noisy unstructured marker estimates in real-time.
We deliver robust marker-based Motion Capture (MoCap) even when using sparse affordable sensors.
arXiv Detail & Related papers (2023-09-25T17:55:24Z) - Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
We propose predictable MDP abstraction (PMA)
Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space.
We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
arXiv Detail & Related papers (2023-02-08T07:37:51Z) - MoEfication: Conditional Computation of Transformer Models for Efficient
Inference [66.56994436947441]
Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost.
We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon.
We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
arXiv Detail & Related papers (2021-10-05T02:14:38Z) - Sufficiently Accurate Model Learning for Planning [119.80502738709937]
This paper introduces the constrained Sufficiently Accurate model learning approach.
It provides examples of such problems, and presents a theorem on how close some approximate solutions can be.
The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
arXiv Detail & Related papers (2021-02-11T16:27:31Z) - Model-Augmented Q-learning [112.86795579978802]
We propose a MFRL framework that is augmented with the components of model-based RL.
Specifically, we propose to estimate not only the $Q$-values but also both the transition and the reward with a shared network.
We show that the proposed scheme, called Model-augmented $Q$-learning (MQL), obtains a policy-invariant solution which is identical to the solution obtained by learning with true reward.
arXiv Detail & Related papers (2021-02-07T17:56:50Z) - Fast and Robust Cascade Model for Multiple Degradation Single Image
Super-Resolution [2.1574781022415364]
Single Image Super-Resolution (SISR) is one of the low-level computer vision problems that has received increased attention in the last few years.
Here, we propose a new formulation of the Convolutional Neural Network (CNN) cascade model.
A new densely connected CNN-architecture is proposed where the output of each sub- module is restricted using some external knowledge.
arXiv Detail & Related papers (2020-11-16T18:59:49Z) - Dynamic Model Pruning with Feedback [64.019079257231]
We propose a novel model compression method that generates a sparse trained model without additional overhead.
We evaluate our method on CIFAR-10 and ImageNet, and show that the obtained sparse models can reach the state-of-the-art performance of dense models.
arXiv Detail & Related papers (2020-06-12T15:07:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.