In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces.
To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives.
In this work, we propose a new algorithm for circuit routing, named Ranking Cost, which innovatively combines search-based methods (i. e., A* algorithm) and learning-based methods (i. e., Evolution Strategies) to form an efficient and trainable router.
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations.
For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.
To this end, we comprehensively investigate three types of ranking constraints, i. e., global ranking, class-specific ranking and IoU-guided ranking losses.
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.
Particularly, with the same architecture of PSPNet (ResNet-18), our method outperforms the single-dataset baseline by 5. 65\%, 6. 57\%, and 5. 79\% of mIoU on the validation sets of Cityscapes, BDD100K, CamVid, respectively.
To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.
The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).
After propagating through a random amplifying medium, a squeezed state commonly shows excess noise above the shot-noise level.
Quasi-periodic fast propagating (QFP) waves are often excited by solar flares, and could be trapped in the coronal structure with low Alfv\'en speed, so they could be used as a diagnosing tool for both the flaring core and magnetic waveguide.
Solar and Stellar Astrophysics
By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.
Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
Thirdly, the agent’s learning process is regarded as a black-box, and the comprehensive metric we proposed is computed after each episode of training, then a Bayesian optimization (BO) algorithm is adopted to guide the agent to evolve towards improving the quality of the approximated Pareto frontier.
This paper presents a novel implicit process-based meta-learning (IPML) algorithm that, in contrast to existing works, explicitly represents each task as a continuous latent vector and models its probabilistic belief within the highly expressive IP framework.
Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally.
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID).
Ranked #2 on Cross-Modal Person Re-Identification on SYSU-MM01
It is a common practice to exploit pyramidal feature representation to tackle the problem of scale variation in object instances.
The emergence of heterogeneous memory (HM) brings a solution to significantly increase memory capacity and break the above tradeoff: Using HM, billions of data points can be placed in the main memory on a single machine without using any data compression.
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
How to collect informative trajectories of which the corresponding context reflects the specification of tasks?
In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution.
Evaluating with 20, 480 input problems, we show that Smartfluidnet achieves 1. 46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.
It has been well recognized that modeling human-object or object-object relations would be helpful for detection task.
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #9 on Skeleton Based Action Recognition on NTU RGB+D
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices.
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation.
Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible.
This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, dense-captioning events in videos, and spatio-temporal action localization.
In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models.
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems.
The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace.
The control module which is based on reinforcement learning then makes a control decision based on these features.
These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes.
The RTP initializes action proposals of the start frame through a Region Proposal Network and then estimates the movements of proposals in next frame in a recurrent manner.
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.
In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix.
Ranked #45 on Image Super-Resolution on BSD100 - 4x upscaling
First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings.
In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.