Search Results for author:
Found 19 papers, 8 papers with code
Memory-augmented Attention Modelling for Videos
We present a method to improve video description generation by modeling higher-order interactions between video frames and described concepts.
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of instantaneous input signal-to-noise ratios.
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate.
Differentiable Greedy Networks
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.
P3O: Policy-on Policy-off Policy Optimization
Extensive experiments on the Atari-2600 and MuJoCo benchmark suites show that this simple technique is effective in reducing the sample complexity of state-of-the-art algorithms.
Meta-Q-Learning
This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL).
TraDE: Transformers for Density Estimation
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators.
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity.
Regioned Episodic Reinforcement Learning
Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.
TraDE: A Simple Self-Attention-Based Density Estimator
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.
Explore with Dynamic Map: Graph Structured Reinforcement Learning
In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration.
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.
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
Goal-oriented Reinforcement Learning (GoRL) is a promising approach for scaling up RL techniques on sparse reward environments requiring long horizon planning.
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.
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
Real-world deployment of machine learning models is challenging because data evolves over time.
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e. g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data.
