Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

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Deep Residual Learning for Image Recognition

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. 
The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Although reinforcement learning has seen tremendous success recently, this kind of trial-and-error learning can be impractical or inefficient in complex environments. The use of demonstrations, on the other hand, enables agents to benefit from expert knowledge rather than having to discover the best action to take through exploration. In this survey, we discuss the advantages of using demonstrations in sequential decision making, various ways to apply demonstrations in learning-based decision making paradigms (for example, reinforcement learning and planning in the learned models), and how to collect the demonstrations in various scenarios. Additionally, we exemplify a practical pipeline for generating and utilizing demonstrations in the recently proposed ManiSkill robot learning benchmark.

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i − z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

Learning generalizable manipulation skills is central for robots to achieve task automation in environments with endless scene and object variations. However, existing robot learning environments are limited in both scale and diversity of 3D assets (especially of articulated objects), making it difficult to train and evaluate the generalization ability of agents over novel objects. In this work, we focus on object-level generalization and propose SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill), a large-scale learning-from-demonstrations benchmark for articulated object manipulation with 3D visual input (point cloud and RGB-D image). ManiSkill supports object-level variations by utilizing a rich and diverse set of articulated objects, and each task is carefully designed for learning manipulations on a single category of objects. We equip ManiSkill with a large number of high-quality demonstrations to facilitate learning-from-demonstrations approaches and perform evaluations on baseline algorithms. We believe that ManiSkill can encourage the robot learning community to explore more on learning generalizable object manipulation skills.

ManiSkill: Learning-from-Demonstrations Benchmark for Generalizable Manipulation Skills.

In exoskeleton research, transparency is the degree to which a device hinders the movement of the user, a critical component of performance and usability. Transparency is most often evaluated individually, thus lacking generalization. Our goal was to systematically evaluate transparency due to inertial effects on gait of a hypothetical hip exoskeleton. We predicted that the weight distribution around the pelvis and the amount of weight applied would change gait characteristics. We instructed 21 healthy individuals to walk on a treadmill while bearing weights on the pelvis between 4 and 8 kg in three different configurations, bilaterally, unilaterally (left side) and on the lumbar portion of the back (L4). We measured kinematics, kinetics, and muscle activity during randomly ordered trials of 1.5 min at typical walking speed. We also calculated the margin of stability to measure medial-lateral stability. We observed that loading the hips bilaterally with 4 kg had no changes in kinematics, kinetics, dynamic stability, or muscle activity, but above 6 kg, sagittal joint power was increased. Loading the lumbar area increased posterior pelvic tilt at 6 kg and decreased dynamic stability at 4 kg, with many individuals reporting some discomfort. For the unilateral placement, above 4 kg dynamic stability was decreased and hip joint power was increased, and above 6 kg the pelvis begins to dip towards the loaded side. These results show the different effects of weight distribution around the pelvis. This study represents a novel, systematic approach to characterizing transparency in exoskeleton design (clinicaltrials.gov: NCT05120115).

The effect of hip exoskeleton weight on kinematics, kinetics, and electromyography during human walking.

In this study, a novel three-stage partial-denitrification/partial-nitrification/anammox (PD/PN/Anammox) system was established. Results showed that the excess of COD and the reduction of nitrite in PD process caused by the instability of influent nitrate are avoided through real-time influent COD/N ratio of 2.1. The extending the aeration interval in PN process enhanced the inhibition of nitrite oxidizing bacteria and promoted the activity of ammonia oxidizing bacteria under low free ammonia (FA, 1.1 ± 0.2 mg/L). In the upflow porous-plate anaerobic reactor (UPPAR), anammox bacteria (Candidatus_Kuennia) was rapid natural enriched from 0 % to 48.4 %, and granular anammox was formed by the increased of filamentous bacteria, α-d-glucose and a-helix. Moreover, the removal and robustness of ammonia nitrogen (98.9 ± 1.8 %) and total nitrogen (87.0 ± 2.3 %) in this system are better than those of the single stage PN/PD/Anammox process, and its cost of oxygen consumption, COD consumption and sludge treatment is far lower than that of nitrification denitrification process and PN/Anammox/denitrification process. This study provides a series of feasible start-up and operation strategies of PD, PN, Anammox and PD/PN/Anammox processes for the application of anammox in rare earth mining wastewater, which proposes upgrading and renovation plans for local wastewater treatment plants. 

Excellent, steady and economical nitrogen removal in highly variable mining wastewater via three-stage partial-denitrification/partial-nitrification/anammox system: A pilot-scale demonstration

Hao Su

Papers

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

ManiSkill: Learning-from-Demonstrations Benchmark for Generalizable Manipulation Skills.

The effect of hip exoskeleton weight on kinematics, kinetics, and electromyography during human walking.

Excellent, steady and economical nitrogen removal in highly variable mining wastewater via three-stage partial-denitrification/partial-nitrification/anammox system: A pilot-scale demonstration