Search Results for author:
Found 31 papers, 11 papers with code
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach
Compared to previous heuristic ALS methods, the guarantee of optimality achieves a more controllable trade-off between circuit complexity and accuracy.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
In this paper, we tackle the fundamental dilemma of offline-to-online fine-tuning: if the agent remains pessimistic, it may fail to learn a better policy, while if it becomes optimistic directly, performance may suffer from a sudden drop.
Digital-analog hybrid matrix multiplication processor for optical neural networks
Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words.
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms
This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies.
VectorTalker: SVG Talking Face Generation with Progressive Vectorisation
To address these, we propose a novel scalable vector graphic reconstruction and animation method, dubbed VectorTalker.
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications
To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency.
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Specifically, we design a prompt template for reasoning that learns from the memory buffer and plans a future trajectory over a long horizon ("reason for future").
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important.
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration.
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations.
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting
We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability.
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
We show that such lossless primitives can drastically improve the performance of hierarchical policies.
On the Role of Discount Factor in Offline Reinforcement Learning
The discount factor, $\gamma$, plays a vital role in improving online RL sample efficiency and estimation accuracy, but the role of the discount factor in offline RL is not well explored.
Optimizing Binary Decision Diagrams with MaxSAT for classification
Then, we lift the encoding to a MaxSAT model to learn optimal BDDs in limited depths, that maximize the number of examples correctly classified.
Offline Reinforcement Learning with Value-based Episodic Memory
Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.
On the Estimation Bias in Double Q-Learning
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.
Generalizable Episodic Memory for Deep Reinforcement Learning
Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning.
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
Surface Dyakonov-Cherenkov Radiation
Recent advances in engineered material technologies (e. g., photonic crystals, metamaterials, plasmonics, etc) provide valuable tools to control Cherenkov radiation.
Optics
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2
SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands.
GraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task
Multi-label networks with branches are proved to perform well in both accuracy and speed, but lacks flexibility in providing dynamic extension onto new labels due to the low efficiency of re-work on annotating and training.
Companion Surface of Danger Cylinder and its Role in Solution Variation of P3P Problem
And when the optical center moves on the danger cylinder, accordingly the optical centers of the two other solutions of the corresponding P3P problem form a new surface, characterized by a polynomial equation of degree 12 in the optical center coordinates, called the Companion Surface of Danger Cylinder (CSDC).
Learning to Adaptively Scale Recurrent Neural Networks
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series.
New insights on Multi-Solution Distribution of the P3P Problem
In this work, we show that when the optical center is outside of all the 6 toroids defined by the control point triangle, each positive root of the Grunert's quartic equation must correspond to a true solution of the P3P problem, and the corresponding P3P problem cannot have a unique solution, it must have either 2 positive solutions or 4 positive solutions.
Geometric Interpretation of side-sharing and point-sharing solutions in the P3P Problem
In this work, we provide some new geometric interpretations on the multi-solution phenomenon in the P3P problem, our main results include: (1): The necessary and sufficient condition for the P3P problem to have a pair of side-sharing solutions is the two optical centers of the solutions both lie on one of the 3 vertical planes to the base plane of control points; (2): The necessary and sufficient condition for the P3P problem to have a pair of point-sharing solutions is the two optical centers of the solutions both lie on one of the 3 so-called skewed danger cylinders;(3): If the P3P problem has other solutions in addition to a pair of side-sharing ( point-sharing) solutions, these remaining solutions must be a point-sharing ( side-sharing ) pair.
Image Classification Based on Quantum KNN Algorithm
However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming.
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.
State-Frequency Memory Recurrent Neural Networks
Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community.
InAR:Inverse Augmented Reality
Augmented reality is the art to seamlessly fuse virtual objects into real ones.
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos
With the prevalence of the commodity depth cameras, the new paradigm of user interfaces based on 3D motion capturing and recognition have dramatically changed the way of interactions between human and computers.
