pwc-archive/papers-with-abstracts huggingface dataset:
Model-free Policy Learning with Reward Gradients
Authors: Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, A. Rupam Mahmood
Date: 2021-03-09 00:00:00
Tasks: Continuous Control, model, MuJoCo, Policy Gradient Methods

Despite the increasing popularity of policy gradient methods, they are yet to be widely utilized in sample-scarce applications, such as robotics. The sample efficiency could be improved by making best usage of available information. As a key component in reinforcement learning, the reward function is usually devised carefully to guide the agent. Hence, the reward function is usually known, allowing access to not only scalar reward signals but also reward gradients. To benefit from reward gradients, previous works require the knowledge of environment dynamics, which are hard to obtain. In this work, we develop the \textit{Reward Policy Gradient} estimator, a novel approach that integrates reward gradients without learning a model. Bypassing the model dynamics allows our estimator to achieve a better bias-variance trade-off, which results in a higher sample efficiency, as shown in the empirical analysis. Our method also boosts the performance of Proximal Policy Optimization on different MuJoCo control tasks.
NeX: Real-time View Synthesis with Neural Basis Expansion
Authors: Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, Supasorn Suwajanakorn
Date: 2021-03-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Novel View Synthesis

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects -- in real time. Unlike traditional MPI that uses a set of simple RGB$\alpha$ planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forward-facing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000$\times$ faster rendering time than the state of the art. For real-time demos, visit https://nex-mpi.github.io/
Proof-of-Learning: Definitions and Practice
Authors: Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot
Date: 2021-03-09 00:00:00

Training machine learning (ML) models typically involves expensive iterative optimization. Once the model's final parameters are released, there is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. Such a mechanism would support security of ML applications in several ways. For instance, it would simplify ownership resolution when multiple parties contest ownership of a specific model. It would also facilitate the distributed training across untrusted workers where Byzantine workers might otherwise mount a denial-of-service by returning incorrect model updates. In this paper, we remediate this problem by introducing the concept of proof-of-learning in ML. Inspired by research on both proof-of-work and verified computations, we observe how a seminal training algorithm, stochastic gradient descent, accumulates secret information due to its stochasticity. This produces a natural construction for a proof-of-learning which demonstrates that a party has expended the compute require to obtain a set of model parameters correctly. In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself. We also instantiate a concrete proof-of-learning mechanism in both of the scenarios described above. In model ownership resolution, it protects the intellectual property of models released publicly. In distributed training, it preserves availability of the training procedure. Our empirical evaluation validates that our proof-of-learning mechanism is robust to variance induced by the hardware (ML accelerators) and software stacks.
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
Authors: Renyi Chen, Molei Tao
Date: 2021-03-09 00:00:00
Tasks: Numerical Integration, Prediction, Time Series Analysis

We consider the learning and prediction of nonlinear time series generated by a latent symplectic map. A special case is (not necessarily separable) Hamiltonian systems, whose solution flows give such symplectic maps. For this special case, both generic approaches based on learning the vector field of the latent ODE and specialized approaches based on learning the Hamiltonian that generates the vector field exist. Our method, however, is different as it does not rely on the vector field nor assume its existence; instead, it directly learns the symplectic evolution map in discrete time. Moreover, we do so by representing the symplectic map via a generating function, which we approximate by a neural network (hence the name GFNN). This way, our approximation of the evolution map is always \emph{exactly} symplectic. This additional geometric structure allows the local prediction error at each step to accumulate in a controlled fashion, and we will prove, under reasonable assumptions, that the global prediction error grows at most \emph{linearly} with long prediction time, which significantly improves an otherwise exponential growth. In addition, as a map-based and thus purely data-driven method, GFNN avoids two additional sources of inaccuracies common in vector-field based approaches, namely the error in approximating the vector field by finite difference of the data, and the error in numerical integration of the vector field for making predictions. Numerical experiments further demonstrate our claims.
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
Authors: Yinan He, Bei Gan, Siyu Chen, Yichun Zhou, Guojun Yin, Luchuan Song, Lu Sheng, Jing Shao, Ziwei Liu
Date: 2021-03-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Benchmarking, Classification, General Classification, Temporal Forgery Localization

The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis have become a pressing issue. However, existing face forgery datasets either have limited diversity or only support coarse-grained analysis. To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 videos), manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations) and annotations (6.3 million classification labels, 2.9 million manipulated area annotations and 221,247 temporal forgery segment labels). We perform extensive benchmarking and studies of existing face forensics methods and obtain several valuable observations.
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression
Authors: Yehuda Dar, Daniel LeJeune, Richard G. Baraniuk
Date: 2021-03-09 00:00:00
Tasks: Philosophy, regression, Transfer Learning

We study a fundamental transfer learning process from source to target linear regression tasks, including overparameterized settings where there are more learned parameters than data samples. The target task learning is addressed by using its training data together with the parameters previously computed for the source task. We define a transfer learning approach to the target task as a linear regression optimization with a regularization on the distance between the to-be-learned target parameters and the already-learned source parameters. We analytically characterize the generalization performance of our transfer learning approach and demonstrate its ability to resolve the peak in generalization errors in double descent phenomena of the minimum L2-norm solution to linear regression. Moreover, we show that for sufficiently related tasks, the optimally tuned transfer learning approach can outperform the optimally tuned ridge regression method, even when the true parameter vector conforms to an isotropic Gaussian prior distribution. Namely, we demonstrate that transfer learning can beat the minimum mean square error (MMSE) solution of the independent target task. Our results emphasize the ability of transfer learning to extend the solution space to the target task and, by that, to have an improved MMSE solution. We formulate the linear MMSE solution to our transfer learning setting and point out its key differences from the common design philosophy to transfer learning.
Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
Authors: Shijie Li, Neel Dey, Katharina Bermond, Leon von der Emde, Christine A. Curcio, Thomas Ach, Guido Gerig
Date: 2021-03-09 00:00:00
Tasks: Segmentation, Semantic Segmentation

Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort. This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of annotation. Our approach has two key aspects: (1) we construct a graph-theoretic soft-segmentation using individual seeds to be used within a regularizer during training and (2) we use an objective function that enables learning from the constructed soft-labels. We achieve competitive results against the state-of-the-art in point-supervised semantic segmentation on challenging datasets in digital pathology. Finally, we scale our methodology to point-supervised segmentation in 3D fluorescence microscopy volumes, obviating the need for arduous manual volumetric delineation. Our code is freely available.
Challenges for Reinforcement Learning in Healthcare
Authors: Elsa Riachi, Muhammad Mamdani, Michael Fralick, Frank Rudzicz
Date: 2021-03-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.
Discrete Function Bases and Convolutional Neural Networks
Authors: Andreas Stöckel
Date: 2021-03-09 00:00:00

We discuss the notion of "discrete function bases" with a particular focus on the discrete basis derived from the Legendre Delay Network (LDN). We characterize the performance of these bases in a delay computation task, and as fixed temporal convolutions in neural networks. Networks using fixed temporal convolutions are conceptually simple and yield state-of-the-art results in tasks such as psMNIST. Main Results (1) We present a numerically stable algorithm for constructing a matrix of DLOPs L in O(qN) (2) The Legendre Delay Network (LDN) can be used to form a discrete function basis with a basis transformation matrix H in O(qN). (3) If q < 300, convolving with the LDN basis online has a lower run-time complexity than convolving with arbitrary FIR filters. (4) Sliding window transformations exist for some bases (Haar, cosine, Fourier) and require O(q) operations per sample and O(N) memory. (5) LTI systems similar to the LDN can be constructed for many discrete function bases; the LDN system is superior in terms of having a finite impulse response. (6) We compare discrete function bases by linearly decoding delays from signals represented with respect to these bases. Results are depicted in Figure 20. Overall, decoding errors are similar. The LDN basis has the highest and the Fourier and cosine bases have the smallest errors. (7) The Fourier and cosine bases feature a uniform decoding error for all delays. These bases should be used if the signal can be represented well in the Fourier domain. (8) Neural network experiments suggest that fixed temporal convolutions can outperform learned convolutions. The basis choice is not critical; we roughly observe the same performance trends as in the delay task. (9) The LDN is the right choice for small q, if the O(q) Euler update is feasible, and if the low O(q) memory requirement is of importance.
Dory: Overcoming Barriers to Computing Persistent Homology
Authors: Manu Aggarwal, Vipul Periwal
Date: 2021-03-09 00:00:00
Tasks: Topological Data Analysis

Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various applications, computational requirements have limited applications to small data sets of a few thousand points. We present Dory, an efficient and scalable algorithm that can compute the persistent homology of large data sets. Dory uses significantly less memory than published algorithms and also provides significant reductions in the computation time compared to most algorithms. It scales to process data sets with millions of points. As an application, we compute the PH of the human genome at high resolution as revealed by a genome-wide Hi-C data set. Results show that the topology of the human genome changes significantly upon treatment with auxin, a molecule that degrades cohesin, corroborating the hypothesis that cohesin plays a crucial role in loop formation in DNA.
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Authors: Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
Date: 2021-03-09 00:00:00

Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfiguration. This flexibility, however, often comes with significantly reduced performance on memory-bound layers and resource underutilisation due to suboptimal mapping of certain layers on the engine's fixed configuration. In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at run time. We refer to these approaches as on-the-fly. To minimise the negative impact of limited bandwidth on memory-bound layers, we present a novel hardware component that enables the on-chip on-the-fly generation of weights. We further introduce an input selective processing element (PE) design that balances the load between PEs on suboptimally mapped layers. Finally, we present unzipFPGA, a framework to train on-the-fly models and traverse the design space to select the highest performing CNN engine configuration. Quantitative evaluation shows that unzipFPGA yields an average speedup of 2.14x and 71% over optimised status-quo and pruned CNN engines under constrained bandwidth and up to 3.69x higher performance density over the state-of-the-art FPGA-based CNN accelerators.
A Discriminative Vectorial Framework for Multi-modal Feature Representation
Authors: Lei Gao, Ling Guan
Date: 2021-03-09 00:00:00
Tasks: Audio Emotion Recognition, Data Visualization, Emotion Recognition, Face Recognition, Object Recognition, Semantic Similarity, Semantic Textual Similarity

Due to the rapid advancements of sensory and computing technology, multi-modal data sources that represent the same pattern or phenomenon have attracted growing attention. As a result, finding means to explore useful information from these multi-modal data sources has quickly become a necessity. In this paper, a discriminative vectorial framework is proposed for multi-modal feature representation in knowledge discovery by employing multi-modal hashing (MH) and discriminative correlation maximization (DCM) analysis. Specifically, the proposed framework is capable of minimizing the semantic similarity among different modalities by MH and exacting intrinsic discriminative representations across multiple data sources by DCM analysis jointly, enabling a novel vectorial framework of multi-modal feature representation. Moreover, the proposed feature representation strategy is analyzed and further optimized based on canonical and non-canonical cases, respectively. Consequently, the generated feature representation leads to effective utilization of the input data sources of high quality, producing improved, sometimes quite impressive, results in various applications. The effectiveness and generality of the proposed framework are demonstrated by utilizing classical features and deep neural network (DNN) based features with applications to image and multimedia analysis and recognition tasks, including data visualization, face recognition, object recognition; cross-modal (text-image) recognition and audio emotion recognition. Experimental results show that the proposed solutions are superior to state-of-the-art statistical machine learning (SML) and DNN algorithms.
Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency
Authors: Mohammad Mehdi Yadollahi, Farzaneh Shoeleh, Sajjad Dadkhah, Ali A. Ghorbani
Date: 2021-03-09 00:00:00

Deep learning techniques are one of the most significant elements of any Artificial Intelligence (AI) services. Recently, these Machine Learning (ML) methods, such as Deep Neural Networks (DNNs), presented exceptional achievement in implementing human-level capabilities for various predicaments, such as Natural Processing Language (NLP), voice recognition, and image processing, etc. Training these models are expensive in terms of computational power and the existence of enough labelled data. Thus, ML-based models such as DNNs establish genuine business value and intellectual property (IP) for their owners. Therefore the trained models need to be protected from any adversary attacks such as illegal redistribution, reproducing, and derivation. Watermarking can be considered as an effective technique for securing a DNN model. However, so far, most of the watermarking algorithm focuses on watermarking the DNN by adding noise to an image. To this end, we propose a framework for watermarking a DNN model designed for a textual domain. The watermark generation scheme provides a secure watermarking method by combining Term Frequency (TF) and Inverse Document Frequency (IDF) of a particular word. The proposed embedding procedure takes place in the model's training time, making the watermark verification stage straightforward by sending the watermarked document to the trained model. The experimental results show that watermarked models have the same accuracy as the original ones. The proposed framework accurately verifies the ownership of all surrogate models without impairing the performance. The proposed algorithm is robust against well-known attacks such as parameter pruning and brute force attack.
SimTriplet: Simple Triplet Representation Learning with a Single GPU
Authors: Quan Liu, Peter C. Louis, Yuzhe Lu, Aadarsh Jha, Mengyang Zhao, Ruining Deng, Tianyuan Yao, Joseph T. Roland, Haichun Yang, Shilin Zhao, Lee E. Wheless, Yuankai Huo
Date: 2021-03-09 00:00:00
Tasks: Contrastive Learning, GPU, Medical Image Analysis, Representation Learning, Self-Supervised Learning, Triplet

Contrastive learning is a key technique of modern self-supervised learning. The broader accessibility of earlier approaches is hindered by the need of heavy computational resources (e.g., at least 8 GPUs or 32 TPU cores), which accommodate for large-scale negative samples or momentum. The more recent SimSiam approach addresses such key limitations via stop-gradient without momentum encoders. In medical image analysis, multiple instances can be achieved from the same patient or tissue. Inspired by these advances, we propose a simple triplet representation learning (SimTriplet) approach on pathological images. The contribution of the paper is three-fold: (1) The proposed SimTriplet method takes advantage of the multi-view nature of medical images beyond self-augmentation; (2) The method maximizes both intra-sample and inter-sample similarities via triplets from positive pairs, without using negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16GB memory. By learning from 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58% better performance compared with supervised learning. It also achieved 2.13% better performance compared with SimSiam. Our proposed SimTriplet can achieve decent performance using only 1% labeled data. The code and data are available at https://github.com/hrlblab/SimTriple.
Parametrized quantum policies for reinforcement learning
Authors: Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko
Date: 2021-03-09 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Benchmarking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction. Such hybrid systems have already shown the potential to tackle real-world tasks in supervised and generative learning, and recent works have established their provable advantages in special artificial tasks. Yet, in the case of reinforcement learning, which is arguably most challenging and where learning boosts would be extremely valuable, no proposal has been successful in solving even standard benchmarking tasks, nor in showing a theoretical learning advantage over classical algorithms. In this work, we achieve both. We propose a hybrid quantum-classical reinforcement learning model using very few qubits, which we show can be effectively trained to solve several standard benchmarking environments. Moreover, we demonstrate, and formally prove, the ability of parametrized quantum circuits to solve certain learning tasks that are intractable for classical models, including current state-of-art deep neural networks, under the widely-believed classical hardness of the discrete logarithm problem.