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Found 37 papers, 13 papers with code
Learning Multi-agent Multi-machine Tending by Mobile Robots
Robotics can help address the growing worker shortage challenge of the manufacturing industry.
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support.
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
Recent advances in protein language models (PLMs) trained on large corpora of protein sequences offer a potential solution to this problem by scoring proteins according to their biological plausibility (such as the TM-score).
Learning to Play Atari in a World of Tokens
We incorporate a transformer-decoder for auto-regressive world modeling and a transformer-encoder for learning behavior by attending to task-relevant cues in the discrete representation of the world model.
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization.
Spectral Temporal Contrastive Learning
This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts.
Transparent Anomaly Detection via Concept-based Explanations
The task of anomaly detection (AD) focuses on finding whether a given sample follows the learned distribution.
Fairness Under Demographic Scarce Regime
Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.
Transformers in Reinforcement Learning: A Survey
We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization.
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning
Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and utilizing a notion of a guarantee or confidence about the behavior of the system.
Discovering Object-Centric Generalized Value Functions From Pixels
Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards.
Source-free Domain Adaptation Requires Penalized Diversity
In addition, PD is augmented with a weighted MI maximization objective for label distribution shift.
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning.
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates.
Automatic Evaluation of Excavator Operators using Learned Reward Functions
Training novice users to operate an excavator for learning different skills requires the presence of expert teachers.
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
In this work, we employ these two metrics for the first time in NLP.
Learning Latent Structural Causal Models
For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.
Locally Constrained Representations in Reinforcement Learning
This encourages the representations to be driven not only by the value/policy learning but also by an additional loss that constrains the representations from over-fitting to the value loss.
Latent Variable Models for Bayesian Causal Discovery
Learning predictors that do not rely on spurious correlations involves building causal representations.
FHIST: A Benchmark for Few-shot Classification of Histological Images
We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning.
Simple Video Generation using Neural ODEs
Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging.
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene.
Predicting Regional Locust Swarm Distribution with Recurrent Neural Networks
Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people.
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep neural networks.
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
Modelling long-term dependencies is a challenge for recurrent neural networks.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Conditional text-to-image generation is an active area of research, with many possible applications.
Ranked #2 on
Text-to-Image Generation
on GeNeVA (i-CLEVR)
ChatPainter: Improving Text to Image Generation using Dialogue
Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task.
Ranked #24 on
Text-to-Image Generation
on MS COCO
(Inception score metric)
FigureQA: An Annotated Figure Dataset for Visual Reasoning
To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.
Ranked #3 on
Visual Question Answering (VQA)
on FigureQA - test 1
The "something something" video database for learning and evaluating visual common sense
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
Ranked #116 on
Action Recognition
on Something-Something V2
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
RATM: Recurrent Attentive Tracking Model
The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.
FitNets: Hints for Thin Deep Nets
In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student.
