Implicit Training of Energy Model for Structure Prediction
- URL: http://arxiv.org/abs/2211.11649v1
- Date: Mon, 21 Nov 2022 17:08:44 GMT
- Title: Implicit Training of Energy Model for Structure Prediction
- Authors: Shiv Shankar, Vihari Piratla
- Abstract summary: In this work, we argue that the existing inference network based structure prediction methods are indirectly learning to optimize a dynamic loss objective parameterized by the energy model.
We then explore using implicit-gradient based technique to learn the corresponding dynamic objectives.
- Score: 14.360826930970765
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Most deep learning research has focused on developing new model and training
procedures. On the other hand the training objective has usually been
restricted to combinations of standard losses. When the objective aligns well
with the evaluation metric, this is not a major issue. However when dealing
with complex structured outputs, the ideal objective can be hard to optimize
and the efficacy of usual objectives as a proxy for the true objective can be
questionable. In this work, we argue that the existing inference network based
structure prediction methods ( Tu and Gimpel 2018; Tu, Pang, and Gimpel 2020)
are indirectly learning to optimize a dynamic loss objective parameterized by
the energy model. We then explore using implicit-gradient based technique to
learn the corresponding dynamic objectives. Our experiments show that
implicitly learning a dynamic loss landscape is an effective method for
improving model performance in structure prediction.
Related papers
- Goal-conditioned Offline Planning from Curious Exploration [28.953718733443143]
We consider the challenge of extracting goal-conditioned behavior from the products of unsupervised exploration techniques.
We find that conventional goal-conditioned reinforcement learning approaches for extracting a value function and policy fall short in this difficult offline setting.
In order to mitigate their occurrence, we propose to combine model-based planning over learned value landscapes with a graph-based value aggregation scheme.
arXiv Detail & Related papers (2023-11-28T17:48:18Z) - Learn from the Past: A Proxy Guided Adversarial Defense Framework with
Self Distillation Regularization [53.04697800214848]
Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models.
AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting.
We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
arXiv Detail & Related papers (2023-10-19T13:13:41Z) - Learning Objective-Specific Active Learning Strategies with Attentive
Neural Processes [72.75421975804132]
Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting.
We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem.
Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
arXiv Detail & Related papers (2023-09-11T14:16:37Z) - Meta-Learning with Self-Improving Momentum Target [72.98879709228981]
We propose Self-improving Momentum Target (SiMT) to improve the performance of a meta-learner.
SiMT generates the target model by adapting from the temporal ensemble of the meta-learner.
We show that SiMT brings a significant performance gain when combined with a wide range of meta-learning methods.
arXiv Detail & Related papers (2022-10-11T06:45:15Z) - Simplifying Model-based RL: Learning Representations, Latent-space
Models, and Policies with One Objective [142.36200080384145]
We propose a single objective which jointly optimize a latent-space model and policy to achieve high returns while remaining self-consistent.
We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods.
arXiv Detail & Related papers (2022-09-18T03:51:58Z) - DST: Dynamic Substitute Training for Data-free Black-box Attack [79.61601742693713]
We propose a novel dynamic substitute training attack method to encourage substitute model to learn better and faster from the target model.
We introduce a task-driven graph-based structure information learning constrain to improve the quality of generated training data.
arXiv Detail & Related papers (2022-04-03T02:29:11Z) - Conservative Objective Models for Effective Offline Model-Based
Optimization [78.19085445065845]
Computational design problems arise in a number of settings, from synthetic biology to computer architectures.
We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs.
COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
arXiv Detail & Related papers (2021-07-14T17:55:28Z) - Discriminator Augmented Model-Based Reinforcement Learning [47.094522301093775]
It is common in practice for the learned model to be inaccurate, impairing planning and leading to poor performance.
This paper aims to improve planning with an importance sampling framework that accounts for discrepancy between the true and learned dynamics.
arXiv Detail & Related papers (2021-03-24T06:01:55Z) - Model-based Meta Reinforcement Learning using Graph Structured Surrogate
Models [40.08137765886609]
We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics.
Our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.
arXiv Detail & Related papers (2021-02-16T17:21:55Z) - End-Effect Exploration Drive for Effective Motor Learning [0.0]
Key objective in reinforcement learning is to invert a target distribution of effects.
End-effect drives are proposed as an effective way to implement goal-directed motor learning.
arXiv Detail & Related papers (2020-06-29T11:59:34Z)
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.