In this note, a novel observer-based output feedback control approach is proposed to address the distributed optimal output consensus problem of uncertain nonlinear multi-agent systems in the normal form over unbalanced directed graphs.
We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries.
The proposed method assumes each data point is generated by a Laplacian Mixture Model (LMM), where its centers are determined by the corresponding points in other point sets.
To our best knowledge, this is the first gradient-based algorithm that can solve different kinds of BLO problems (e. g., optimistic, pessimistic and with constraints) all with solid convergence guarantees.
In particular, by introducing an auxiliary as initialization to guide the optimization dynamics and designing a pessimistic trajectory truncation operation, we construct a reliable approximate version of the original BLO in the absence of LLC hypothesis.
To investigate the number and distribution of local optima, we conduct a fitness landscape analysis on the sum rate maximization problems.
Adversarial attacks are feasible in the real world for object detection.
Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering.
Bi-level optimization model is able to capture a wide range of complex learning tasks with practical interest.
To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic $l_0$ norm.
We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation.
Although GCHRL possesses superior exploration ability by decomposing tasks via subgoals, existing GCHRL methods struggle in temporally extended tasks with sparse external rewards, since the high-level policy learning relies on external rewards.
To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel.
In this paper, we propose a novel temporal fusion (TF) module to fuse the two-stream joints' information to predict human motion, including a temporal concatenation and a reinforcement trajectory spatial-temporal (TST) block, specifically designed to keep prediction temporal consistency.
In this work, we formulate BLOs from an optimistic bi-level viewpoint and establish a new gradient-based algorithmic framework, named Bi-level Descent Aggregation (BDA), to partially address the above issues.
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community.
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
For many two dimensional van der Waals materials, twisting two adjacent layers with respect to each other leads to flat electronic bands in the two corresponding spatial directions -- a notion sometimes referred to as twistronics as it enables a wealth of physical phenomena.
Materials Science Statistical Mechanics Strongly Correlated Electrons Superconductivity
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications.
However, due to the variety of image degradation types in the real world, models trained on single-modal simulation datasets do not always have good robustness and generalization ability in different degradation scenarios.
Based on the idea of small world network, a random edge adding algorithm is proposed to improve the performance of convolutional neural network model.
The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work.
Objective: With the increase in studies on the T&V of NN-based control software in safety-critical domains, it is important to systematically review the state-of-the-art T&V methodologies, to classify approaches and tools that are invented, and to identify challenges and gaps for future studies.
Alternating Direction Method of Multiplier (ADMM) has been a popular algorithmic framework for separable optimization problems with linear constraints.
Classical optimization techniques often formulate the feasibility of the problems as set, equality or inequality constraints.
no code implementations • 17 Apr 2019 • Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga
The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.
no code implementations • 29 Mar 2019 • Leonardo Rundo, Changhee Han, Jin Zhang, Ryuichiro Hataya, Yudai Nagano, Carmelo Militello, Claudio Ferretti, Marco S. Nobile, Andrea Tangherloni, Maria Carla Gilardi, Salvatore Vitabile, Hideki Nakayama, Giancarlo Mauri
Prostate cancer is the most common cancer among US men.
Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc.
However, in electronic medical records (EMR), the texts containing sentences are shorter than that in general domain, which leads to the lack of semantic features and the ambiguity of semantic.
In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between $d$ possible items; from these we need to predict preferences of the users for items they have not yet seen.