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Found 25 papers, 10 papers with code
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors
In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance.
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
The pursuit of modern models, like Diffusion Models (DMs), holds promise for achieving high-fidelity, real-time speech synthesis.
Exploring Task Difficulty for Few-Shot Relation Extraction
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances.
Analyzing Modality Robustness in Multimodal Sentiment Analysis
In this work, we hope to address that by (i) Proposing simple diagnostic checks for modality robustness in a trained multimodal model.
Generative Prompt Tuning for Relation Classification
Current prompt tuning methods mostly convert the downstream tasks to masked language modeling problems by adding cloze-style phrases and mapping all labels to verbalizations with fixed length, which has proven effective for tasks with simple label spaces.
Ranked #2 on
Relation Extraction
on Re-TACRED
HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks
In this work, we present HyperTTS, which comprises a small learnable network, "hypernetwork", that generates parameters of the Adapter blocks, allowing us to condition Adapters on speaker representations and making them dynamic.
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model
Formally, MPG is constructed as a weighted average of the data-driven and model-driven PGs, where the former is the derivative of the learned Q-value function, and the latter is that of the model-predictive return.
DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images
We propose a deep learning model that can help radiologists and clinicians use chest X-rays to diagnose COVID-19 cases and show the diagnostic features of pneumonia.
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors
Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data.
An End-to-End Multi-task Learning Model for Fact Checking
With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia.
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.
Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems
This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems.
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities.
Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion
The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA).
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph
The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA).
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model to prefer a candidate that is logically consistent with the speaker's history logic.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.
Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers
Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation.
Focus on Local Regions for Query-based Object Detection
We improve the self-attention by isolating connections between irrelevant objects that makes it focus on local regions but not global regions.
From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching
Inverted landing is a routine behavior among a number of animal fliers.
