pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Aggregation Networks for Class-Incremental Learning
Authors: Yaoyao Liu, Bernt Schiele, Qianru Sun
Date: 2020-10-10 00:00:00
Proceeding: CVPR 2021 1
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets), in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances.
COVID-19 Classification Using Staked Ensembles: A Comprehensive Analysis
Authors: Lalith Bharadwaj B, Rohit Boddeda, Sai Vardhan K, Madhu G
Date: 2020-10-07 00:00:00
Tasks: Classification

The issue of COVID-19, increasing with a massive mortality rate. This led to the WHO declaring it as a pandemic. In this situation, it is crucial to perform efficient and fast diagnosis. The reverse transcript polymerase chain reaction (RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is time-consuming and instead chest CT (or Chest X-ray) can be used for a fast and accurate diagnosis. Automated diagnosis is considered to be important as it reduces human effort and provides accurate and low-cost tests. The contributions of our research are three-fold. First, it is aimed to analyse the behaviour and performance of variant vision models ranging from Inception to NAS networks with the appropriate fine-tuning procedure. Second, the behaviour of these models is visually analysed by plotting CAMs for individual networks and determining classification performance with AUCROC curves. Thirdly, stacked ensembles techniques are imparted to provide higher generalisation on combining the fine-tuned models, in which six ensemble neural networks are designed by combining the existing fine-tuned networks. Implying these stacked ensembles provides a great generalization to the models. The ensemble model designed by combining all the fine-tuned networks obtained a state-of-the-art accuracy score of 99.17%. The precision and recall for the COVID-19 class are 99.99% and 89.79% respectively, which resembles the robustness of the stacked ensembles.
W-Net : One-Shot Arbitrary-StyleChinese Character Generationwith Deep Neural Networks
Authors: Haochuan Jiang, Guanyu Yang, Kaizhu Huang, and Rui ZHANG
Date: 2018-12-13 00:00:00

Abstract. Due to the huge category number, the sophisticated com-binations of various strokes and radicals, and the free writing or print-ing styles, generating Chinese characters with diverse styles is alwaysconsidered as a diﬃcult task. In this paper, an eﬃcient and general-ized deep framework, namely, the W-Net, is introduced for the one-shotarbitrary-style Chinese character generation task. Speciﬁcally, given asingle character (one-shot) with a speciﬁc style (e.g., a printed font orhand-writing style), the proposed W-Net model is capable of learningand generating any arbitrary characters sharing the style similar to thegiven single character. Such appealing property was rarely seen in theliterature. We have compared the proposed W-Net framework to manyother competitive methods. Experimental results showed the proposedmethod is signiﬁcantly superior in the one-shot setting.
InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction
Authors: Hansi Hettiarachchi, Tharindu Ranasinghe
Date: 2020-10-11 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set.
Observer-based boundary control of distributed port-Hamiltonian systems
Authors: Jesus Toledo, Yongxin Wu, Hector Ramirez, Yann Le Gorrec
Date: 2019-10-09 00:00:00

An observer-based boundary controller for infinite-dimensional port-Hamiltonian systems defined on 1D spatial domains is proposed. The design is based on an early-lumping approach in which a finite-dimensional approximation of the infinite-dimensional system derived by spatial discretization is used to design the observer and the controller. As long as the finite-dimensional approximation approaches the infinite-dimensional model, the performances also do. The main contribution is a constructive method which guarantees that the interconnection between the controller and the infinite-dimensional system is asymptotically stable. A Timoshenko beam model has been used to illustrate the approach.
BiPointNet: Binary Neural Network for Point Clouds
Authors: Haotong Qin; Zhongang Cai; Mingyuan Zhang;Yifu Ding; Haiyu Zhao; Shuai Yi; Xianglong Liu; Hao Su
Date: 2020-10-12 00:00:00
Tasks: Binarization

To alleviate the resource constraint for real-time point clouds applications that run on edge devices, we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds. In this work, we discover that the immense performance drop of binarized models for point clouds is caused by two main challenges: aggregation-induced feature homogenization that leads to a degradation of information entropy, and scale distortion that hinders optimization and invalidates scale-sensitive structures. With theoretical justifications and in-depth analysis, we propose Entropy-Maximizing Aggregation(EMA) to modulate the distribution before aggregation for the maximum information entropy, andLayer-wise Scale Recovery(LSR) to efficiently restore feature scales. Extensive experiments show that our BiPointNet outperforms existing binarization methods by convincing margins, at the level even comparable with the full precision counterpart. We highlight that our techniques are generic which show significant improvements on various fundamental tasks and mainstream backbones. BiPoint-Net gives an impressive 14.7× speedup and 18.9× storage saving on real-world resource-constrained devices.
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation
Authors: Lucas Teixeira, Martin R. Oswald, Marc Pollefeys, Margarita Chli
Date: 2020-01-17 00:00:00
Tasks: Depth Completion, GPU, Monocular Depth Estimation, Style Transfer

On the pursuit of autonomous flying robots, the scientific community
has been developing onboard real-time algorithms for localisation,
mapping and planning. Despite recent progress, the available solutions
still lack accuracy and robustness in many aspects. While mapping for
autonomous cars had a substantive boost using deep-learning techniques
to enhance LIDAR measurements using image-based depth completion, the
large viewpoint variations experienced by aerial vehicles are still
posing major challenges for learning-based mapping approaches. In this
paper, we propose a depth completion and uncertainty estimation
approach that better handles the challenges of aerial platforms, such
as large viewpoint and depth variations, and limited computing
resources. The core of our method is a novel compact network that
performs both depth completion and confidence estimation using an
image-guided approach. Real-time performance onboard a GPU suitable for
small flying robots is achieved by sharing deep features between both
tasks. Experiments demonstrate that our network outperforms the
state-of-the-art in depth completion and uncertainty estimation for
single-view methods on mobile GPUs. We further present a new
photorealistic aerial depth completion dataset that exhibits more
challenging depth completion scenarios than the established indoor and
car driving datasets. The dataset includes an open-source,
visual-inertial UAV simulator for photo-realistic data generation. Our
results show that our network trained on this dataset can be directly
deployed on real-world outdoor aerial public datasets without
fine-tuning or style transfer.
Open-Domain Question Answering Goes Conversational via Question Rewriting
Authors: Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
Date: 2020-10-10 00:00:00
Proceeding: NAACL 2021 4
Tasks: Conversational Question Answering, Open-Domain Question Answering, Passage Retrieval, Question Answering, Question Rewriting, Reading Comprehension, Retrieval

We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading
Authors: Anand Bhattad, David A. Forsyth
Date: 2020-10-12 00:00:00
Tasks: Image Harmonization, Neural Rendering, Object

We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene. Rendering objects using an illumination model of the scene doesn't work, because doing so requires a geometric and material model of the object, which is hard to recover from a single image. In this paper, we introduce a method that corrects shading inconsistencies of the inserted object without requiring a geometric and physical model or an environment map. Our method uses a deep image prior (DIP), trained to produce reshaded renderings of inserted objects via consistent image decomposition inferential losses. The resulting image from DIP aims to have (a) an albedo similar to the cut-and-paste albedo, (b) a similar shading field to that of the target scene, and (c) a shading that is consistent with the cut-and-paste surface normals. The result is a simple procedure that produces convincing shading of the inserted object. We show the efficacy of our method both qualitatively and quantitatively for several objects with complex surface properties and also on a dataset of spherical lampshades for quantitative evaluation. Our method significantly outperforms an Image Harmonization (IH) baseline for all these objects. They also outperform the cut-and-paste and IH baselines in a user study with over 100 users.
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Authors: Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi
Date: 2020-10-12 00:00:00
Proceeding: EMNLP 2020 11
Tasks: counterfactual, Counterfactual Reasoning, Text Generation, Text Infilling

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are trained either to condition only on the past context or to perform narrowly scoped text-infilling. In this paper, we propose DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision. The key intuition of our algorithm is incorporating the future through back-propagation, during which, we only update the internal representation of the output while fixing the model parameters. By alternating between forward and backward propagation, DeLorean can decode the output representation that reflects both the left and right contexts. We demonstrate that our approach is general and applicable to two nonmonotonic reasoning tasks: abductive text generation and counterfactual story revision, where DeLorean outperforms a range of unsupervised and some supervised methods, based on automatic and human evaluation.
Multi-Stage Pre-training for Low-Resource Domain Adaptation
Authors: Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avirup Sil, Todd Ward
Date: 2020-10-12 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Document Ranking, Domain Adaptation, Language Modeling, Language Modelling, Reading Comprehension, Transfer Learning

Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pre-trained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pre-trained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Authors: Tal Reiss, Niv Cohen, Liron Bergman, Yedid Hoshen
Date: 2020-10-12 00:00:00
Proceeding: CVPR 2021 1
Tasks: Anomaly Detection, Anomaly Segmentation, Continual Learning, Multi-class Classification, One-Class Classification, Transfer Learning

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pretrained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pretrained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gains in anomaly detection, we adapt pretrained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins.
Nearly Minimax Optimal Reward-free Reinforcement Learning
Authors: Zihan Zhang, Simon S. Du, Xiangyang Ji
Date: 2020-10-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple reward functions. This framework has two phases. In the exploration phase, the agent collects trajectories by interacting with the environment without using any reward signal. In the planning phase, the agent needs to return a near-optimal policy for arbitrary reward functions. We give a new efficient algorithm, \textbf{S}taged \textbf{S}ampling + \textbf{T}runcated \textbf{P}lanning (\algoname), which interacts with the environment at most $O\left( \frac{S^2A}{\epsilon^2}\text{poly}\log\left(\frac{SAH}{\epsilon}\right) \right)$ episodes in the exploration phase, and guarantees to output a near-optimal policy for arbitrary reward functions in the planning phase. Here, $S$ is the size of state space, $A$ is the size of action space, $H$ is the planning horizon, and $\epsilon$ is the target accuracy relative to the total reward. Notably, our sample complexity scales only \emph{logarithmically} with $H$, in contrast to all existing results which scale \emph{polynomially} with $H$. Furthermore, this bound matches the minimax lower bound $\Omega\left(\frac{S^2A}{\epsilon^2}\right)$ up to logarithmic factors. Our results rely on three new techniques : 1) A new sufficient condition for the dataset to plan for an $\epsilon$-suboptimal policy; 2) A new way to plan efficiently under the proposed condition using soft-truncated planning; 3) Constructing extended MDP to maximize the truncated accumulative rewards efficiently.
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Authors: Paria Rashidinejad, Jiantao Jiao, Stuart Russell
Date: 2020-10-12 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: PAC learning

We present an efficient and practical (polynomial time) algorithm for online prediction in unknown and partially observed linear dynamical systems (LDS) under stochastic noise. When the system parameters are known, the optimal linear predictor is the Kalman filter. However, the performance of existing predictive models is poor in important classes of LDS that are only marginally stable and exhibit long-term forecast memory. We tackle this problem through bounding the generalized Kolmogorov width of the Kalman filter model by spectral methods and conducting tight convex relaxation. We provide a finite-sample analysis, showing that our algorithm competes with Kalman filter in hindsight with only logarithmic regret. Our regret analysis relies on Mendelson's small-ball method, providing sharp error bounds without concentration, boundedness, or exponential forgetting assumptions. We also give experimental results demonstrating that our algorithm outperforms state-of-the-art methods. Our theoretical and experimental results shed light on the conditions required for efficient probably approximately correct (PAC) learning of the Kalman filter from partially observed data.
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Authors: Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Xiaoning Qian
Date: 2020-10-12 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Data Integration, Relational Reasoning, Representation Learning

High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian representation learning method that infers the relational interactions across multi-omics data types. Our method, Bayesian Relational Learning (BayReL) for multi-omics data integration, takes advantage of a priori known relationships among the same class of molecules, modeled as a graph at each corresponding view, to learn view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL in inferring meaningful interactions compared to existing baselines.
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
Authors: Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso
Date: 2020-10-12 00:00:00
Tasks: CPU, Recommendation Systems

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.
Large-Scale Methods for Distributionally Robust Optimization
Authors: Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
Date: 2020-10-12 00:00:00
Proceeding: NeurIPS 2020 12

We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $\chi^2$ divergence uncertainty sets. We prove that our algorithms require a number of gradient evaluations independent of training set size and number of parameters, making them suitable for large-scale applications. For $\chi^2$ uncertainty sets these are the first such guarantees in the literature, and for CVaR our guarantees scale linearly in the uncertainty level rather than quadratically as in previous work. We also provide lower bounds proving the worst-case optimality of our algorithms for CVaR and a penalized version of the $\chi^2$ problem. Our primary technical contributions are novel bounds on the bias of batch robust risk estimation and the variance of a multilevel Monte Carlo gradient estimator due to [Blanchet & Glynn, 2015]. Experiments on MNIST and ImageNet confirm the theoretical scaling of our algorithms, which are 9--36 times more efficient than full-batch methods.