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Found 20 papers, 11 papers with code
Deep Directed Generative Models with Energy-Based Probability Estimation
Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training.
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition
Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability.
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
Bayesian Model-Agnostic Meta-Learning
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.
Fast AutoAugment
Data augmentation is an essential technique for improving generalization ability of deep learning models.
Ranked #3 on
Image Classification
on SVHN
Edge-labeling Graph Neural Network for Few-shot Learning
In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.
Scalable Neural Architecture Search for 3D Medical Image Segmentation
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.
Variational Temporal Abstraction
We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data.
Visual Concept Reasoning Networks
It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties.
Flexible Model Aggregation for Quantile Regression
Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being overly reductive.
Faster Deep Reinforcement Learning with Slower Online Network
In this paper we endow two popular deep reinforcement learning algorithms, namely DQN and Rainbow, with updates that incentivize the online network to remain in the proximity of the target network.
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
We pose two hypotheses: (1) task-agnostic methods might provide advantages in settings with limited data, computation, or high dimensionality, and (2) faster adaptation may be particularly beneficial in continual learning settings, helping to mitigate the effects of catastrophic forgetting.
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning.
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
In this paper, we propose Complementary Domain Adaptation and Generalization (CoDAG), a simple yet effective learning framework that combines domain adaptation and generalization in a complementary manner to achieve three major goals of unsupervised continual domain shift learning: adapting to a current domain, generalizing to unseen domains, and preventing forgetting of previously seen domains.
Ranked #1 on
Unsupervised Continual Domain Shift Learning
on PACS
Domain Generalization
Unsupervised Continual Domain Shift Learning
+1
Meta-Learning with a Geometry-Adaptive Preconditioner
In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric.
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios
Parameter-efficient fine-tuning (PEFT) techniques, such as adapter tuning, aim to fine-tune a pre-trained language model (PLM) using a minimal number of parameters for a specific task or profile.
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization
In this paper, we address a representative case of data inequality problem across domains termed Semi-Supervised Domain Generalization (SSDG), in which only one domain is labeled while the rest are unlabeled.
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text.
