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Found 20 papers, 14 papers with code
Whisper in Medusa's Ear: Multi-head Efficient Decoding for Transformer-based ASR
Large transformer-based models have significant potential for speech transcription and translation.
WhisperNER: Unified Open Named Entity and Speech Recognition
In this paper, we introduce WhisperNER, a novel model that allows joint speech transcription and entity recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Keyword-Guided Adaptation of Automatic Speech Recognition
Automatic Speech Recognition (ASR) technology has made significant progress in recent years, providing accurate transcription across various domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions.
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
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
We then condition a diffusion model on the video and use the extracted text through a classifier-guidance mechanism where a pre-trained ASR serves as the classifier.
DisCLIP: Open-Vocabulary Referring Expression Generation
Our results highlight the potential of using pre-trained visual-semantic models for generating high-quality contextual descriptions.
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.
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 #2 on
Multi-Task Learning
on Cityscapes test
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects.
Ranked #2 on
Group Activity Recognition
on Collective Activity
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
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.
Learning Object Permanence from Video
The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object.
Ranked #3 on
Video Object Tracking
on CATER
