Learning Functions on Multiple Sets using Multi-Set Transformers
- URL: http://arxiv.org/abs/2206.15444v1
- Date: Thu, 30 Jun 2022 17:39:15 GMT
- Title: Learning Functions on Multiple Sets using Multi-Set Transformers
- Authors: Kira Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh and
Pascal Poupart
- Abstract summary: We show how to generalize this architecture to sets of elements of any dimension by dimension equivariance.
We demonstrate that our architecture is a universal approximator of these functions, and show superior results to existing methods on a variety of tasks.
- Score: 31.09791656949115
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a general deep architecture for learning functions on multiple
permutation-invariant sets. We also show how to generalize this architecture to
sets of elements of any dimension by dimension equivariance. We demonstrate
that our architecture is a universal approximator of these functions, and show
superior results to existing methods on a variety of tasks including counting
tasks, alignment tasks, distinguishability tasks and statistical distance
measurements. This last task is quite important in Machine Learning. Although
our approach is quite general, we demonstrate that it can generate approximate
estimates of KL divergence and mutual information that are more accurate than
previous techniques that are specifically designed to approximate those
statistical distances.
Related papers
- A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset [44.94304541427113]
We propose a multitask deep learning model to perform multiple classification and regression tasks simultaneously on hyperspectral images.
We validated our approach on a large hyperspectral dataset called TAIGA.
A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.
arXiv Detail & Related papers (2024-07-23T11:14:54Z) - InterroGate: Learning to Share, Specialize, and Prune Representations
for Multi-task Learning [17.66308231838553]
We propose a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency.
We employ a learnable gating mechanism to automatically balance the shared and task-specific representations while preserving the performance of all tasks.
arXiv Detail & Related papers (2024-02-26T18:59:52Z) - Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning [39.4348419684885]
Multi-task learning (MTL) aims at learning a single model that solves several tasks efficiently.
We introduce a novel gradient aggregation approach using Bayesian inference.
We empirically demonstrate the benefits of our approach in a variety of datasets.
arXiv Detail & Related papers (2024-02-06T14:00:43Z) - Distribution Matching for Multi-Task Learning of Classification Tasks: a
Large-Scale Study on Faces & Beyond [62.406687088097605]
Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space.
We show that MTL can be successful with classification tasks with little, or non-overlapping annotations.
We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
arXiv Detail & Related papers (2024-01-02T14:18:11Z) - Multi-task Bias-Variance Trade-off Through Functional Constraints [102.64082402388192]
Multi-task learning aims to acquire a set of functions that perform well for diverse tasks.
In this paper we draw intuition from the two extreme learning scenarios -- a single function for all tasks, and a task-specific function that ignores the other tasks.
We introduce a constrained learning formulation that enforces domain specific solutions to a central function.
arXiv Detail & Related papers (2022-10-27T16:06:47Z) - Uni-Perceiver: Pre-training Unified Architecture for Generic Perception
for Zero-shot and Few-shot Tasks [73.63892022944198]
We present a generic perception architecture named Uni-Perceiver.
It processes a variety of modalities and tasks with unified modeling and shared parameters.
Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
arXiv Detail & Related papers (2021-12-02T18:59:50Z) - Learning Aggregation Functions [78.47770735205134]
We introduce LAF (Learning Aggregation Functions), a learnable aggregator for sets of arbitrary cardinality.
We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures.
arXiv Detail & Related papers (2020-12-15T18:28:53Z) - Multi-task Supervised Learning via Cross-learning [102.64082402388192]
We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks.
In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other.
This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
arXiv Detail & Related papers (2020-10-24T21:35:57Z) - Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint.
We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
arXiv Detail & Related papers (2020-04-28T09:15:50Z)
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.