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Found 17 papers, 10 papers with code
Multi Task Inverse Reinforcement Learning for Common Sense Reward
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function.
Improved Generalization of Weight Space Networks via Augmentations
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of neural networks.
Data Augmentations in Deep Weight Spaces
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization.
Combining Language Models For Specialized Domains: A Colorful Approach
In this work, we introduce a novel approach that integrates domain-specific or secondary LM into general-purpose LM.
Equivariant Deep Weight Space Alignment
To accelerate the alignment process and improve its quality, we propose a novel framework aimed at learning to solve the weight alignment problem, which we name Deep-Align.
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization
Open vocabulary keyword spotting is a crucial and challenging task in automatic speech recognition (ASR) that focuses on detecting user-defined keywords within a spoken utterance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning Discrete Weights and Activations Using the Local Reparameterization Trick
In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference.
Auxiliary Learning as an Asymmetric Bargaining Game
Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets.
Equivariant Architectures for Learning in Deep Weight Spaces
Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction.
A Study on the Evaluation of Generative Models
In this work, we study the evaluation metrics of generative models by generating a high-quality synthetic dataset on which we can estimate classical metrics for comparison.
Multi-Task Learning as a Bargaining Game
In this paper, we propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.
Ranked #1 on
Multi-Task Learning
on Cityscapes test
Personalized Federated Learning with Gaussian Processes
A key challenge in this setting is to learn effectively across clients even though each client has unique data that is often limited in size.
Ranked #1 on
Personalized Federated Learning
on CIFAR-100
Personalized Federated Learning using Hypernetworks
In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client.
Ranked #1 on
Personalized Federated Learning
on CIFAR-10
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
As a result, our method scales well with both the number of classes and data size.
Learning the Pareto Front with Hypernetworks
Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.
Auxiliary Learning by Implicit Differentiation
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.
Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models
This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients.
