pwc-archive/papers-with-abstracts huggingface dataset:
Training Deep Spiking Neural Networks with Bio-plausible Learning Rules
Authors: Yukun Yang, Peng Li
Date: 2021-09-29 00:00:00

There exists a marked cleavage between the biological plausible approaches and the practical backpropagation-based approaches on how to train a deep spiking neural network (DSNN) with better performance. The well-known bio-plausible learning rule Spike-Timing-Dependent-Plasticity (STDP) cannot explain how the brain adjusts synaptic weights to encode information through accurate spike timing, while the widely applied backpropagation (BP) algorithms lack a biologically credible mechanism. In this work, we wish to answer the question of whether it is possible to train a DSNN using bio-plausible learning rules only and reach the comparable accuracy trained by the BP-based learning rules. We observed that the STDP learning rule calculated between the membrane potential waveform in the apical dendrite of a pyramidal cell and its input spike train with the help of local recurrent connections synthesized by somatostatin (SOM) interneurons is able to perform supervised learning. This architecture is also supported by recent observations of the brain's cortical microcircuits. This new view of how spiking neurons may accurately adjust their complex temporal dynamics with the help of special local feedback connections bridges the performance gap between the bio-plausible approaches and the BP-based approaches and provides a possible answer to how our brain learns. We verify our observation with a simplified spiking neuron model and two different cell types on several datasets and further provide theoretical proof of the equivalence between STDP and BP under a special circumstance.
Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer
Authors: Dennis Y.H. Wu, Dinan Lin, Vincent Chen, Hung-Hsuan Chen
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

This paper studies Associate Learning (AL), an alternative methodology to the end-to-end backpropagation (BP).  We introduce the workflow to convert a neural network into a proper structure such that AL can be used to learn the weights for various types of neural networks.  We compared AL and BP on some of the most successful types of neural networks -- Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer.  Experimental results show that AL consistently outperforms BP on various open datasets.  We discuss possible reasons for AL's success and its limitations.
Learning Predictive, Online Approximations of Explanatory, Offline Algorithms
Authors: Mattson Thieme, Ammar Gilani, Han Liu
Date: 2021-09-29 00:00:00
Tasks: Multi-Task Learning

In this work, we introduce a general methodology for approximating offline algorithms in online settings. By encoding the behavior of offline algorithms in graphs, we train a multi-task learning model to simultaneously detect behavioral structures which have already occurred and predict those that may come next. We demonstrate the methodology on both synthetic data and historical stock market data, where the contrast between explanation and prediction is particularly stark. Taken together, our work represents the first general and end-to-end differentiable approach for generating online approximations of offline algorithms.
Improving Meta-Continual Learning Representations with Representation Replay
Authors: Lawrence Ki-On Chan, James Kwok
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, Meta-Learning

Continual learning often suffers from catastrophic forgetting. Recently, meta-continual learning algorithms use meta-learning to learn how to continually learn. A recent state-of-the-art is online aware meta-learning (OML). This can be further improved by incorporating experience replay (ER) into its meta-testing. However, the use of ER only in meta-testing but not in meta-training suggests that the model may not be optimally meta-trained. In this paper, we remove this inconsistency in the use of ER and improve continual learning representations by integrating ER also into meta-training. We propose to store the samples' representations, instead of the samples themselves, into the replay buffer. This ensures the batch nature of ER  does not conflict with the online-aware nature of OML. Moreover, we introduce a meta-learned sample selection scheme to replace the widely used reservoir sampling to populate the replay buffer. This allows the most significant samples to be stored, rather than relying on randomness. Class-balanced modifiers are further added to the sample selection scheme to ensure each class has sufficient samples stored in the replay buffer. Experimental results on a number of real-world meta-continual learning benchmark data sets demonstrate that the proposed method outperforms the state-of-the-art. Moreover, the learned representations have better clustering structures and are more discriminative.
Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution
Authors: Guojun Wu, Yun Yue, Yanhua Li, Ziming Zhang
Date: 2021-09-29 00:00:00

Lightweight neural networks refer to deep networks with small numbers of parameters, which are allowed to be implemented in resource-limited hardware such as embedded systems. To learn such lightweight networks effectively and efficiently, in this paper we propose a novel convolutional layer, namely {\em Channel-Split Recurrent Convolution (CSR-Conv)}, where we split the output channels to generate data sequences with length $T$ as the input to the recurrent layers with shared weights. As a consequence, we can construct lightweight convolutional networks by simply replacing (some) linear convolutional layers with CSR-Conv layers. We prove that under mild conditions the model size decreases with the rate of $O(\frac{1}{T^2})$. Empirically we demonstrate the state-of-the-art performance using VGG-16, ResNet-50, ResNet-56, ResNet-110, DenseNet-40, MobileNet, and EfficientNet as backbone networks on CIFAR-10 and ImageNet.
Improved Generalization Bound for Deep Neural Networks Using Geometric Functional Analysis
Authors: Phani raj Chinnalingu, Rajarshi Banerjee
Date: 2021-09-29 00:00:00
Tasks: Generalization Bounds

Understanding how a neural network behaves in multiple domains is the key to further  its  explainability,  generalizability,  and  robustness. In this paper, we prove a novel generalization bound using the fundamental concepts of geometric functional analysis. Specifically, by leveraging the covering number of the training dataset and applying certain geometric inequalities we show that a sharp bound can be obtained. To the best of our knowledge this is the first approach which utilizes covering numbers to estimate such generalization bounds.
BANANA: a Benchmark for the Assessment of Neural Architectures for Nucleic Acids
Authors: Luca Salvatore Lorello, Andrea Galassi, Paolo Torroni
Date: 2021-09-29 00:00:00
Tasks: Language Modelling

Machine learning has always played an important role in bioinformatics and recent applications of deep learning have allowed solving a new spectrum of biologically relevant tasks.
However, there is still a  gap between the  ``mainstream'' AI and the bioinformatics communities. This is partially due to the format of bioinformatics data, which are typically difficult to process and adapt to machine learning tasks without deep domain knowledge.
Moreover, the lack of standardized evaluation methods makes it difficult to rigorously compare different models and assess their true performance.
To help to bridge this gap, and inspired by work such as SuperGLUE and TAPE, we present BANANA, a benchmark consisting of six supervised classification tasks designed to assess language model performance in the DNA and RNA domains. The tasks are defined over three genomics and one transcriptomics languages (human DNA, bacterial 16S gene, nematoda ITS2 gene, human mRNA) and measure a model's ability to perform whole-sequence classification in a variety of setups.
Each task was built from readily available data and is presented in a ready-to-use format, with defined labels, splits, and evaluation metrics.
We use BANANA to test state-of-the-art NLP architectures, such as Transformer-based models, observing that, in general, self-supervised pretraining without external corpora is beneficial in every task.
Single-Cell Capsule Attention : an interpretable method of cell type classification for single-cell RNA-sequencing data
Authors: Tianxu Wang, Xiuli Ma
Date: 2021-09-29 00:00:00
Tasks: Classification, Diversity, Vocal Bursts Type Prediction

Single-cell RNA-sequencing technique can obtain genes’ expression level of every cell. Cell type classification (also known as cell type annotation) on single-cell RNA-seq data helps to explore cellular heterogeneity and diversity. Previous methods for cell type classification are either based on statistical hypotheses of gene expression or deep neural networks. However, the hypotheses may not reflect the true expression level. Deep neural networks lack interpretation for the result. Here we present an interpretable neural-network based method single-cell capsule attention(scCA) which assigns cells to different cell types based on their different feature patterns. In our model, we first generate capsules which extract different features of the cells. Then we obtain compound features which combine a set of features’ information through a LSTM model. In the end, we train attention weights and apply them to the compound features. scCA provides a strong interpretation for cell type classification result. Cells from the same cell type share a similar pattern of capsules’ relationship and similar distribution of attention weights for compound features. Compared with previous methods for cell type classification on nine datasets, scCA shows high accuracy on all datasets with robustness and reliable interpretation.
Randomized Primal-Dual Coordinate Method for Large-scale Linearly Constrained Nonsmooth Nonconvex Optimization
Authors: Lei Zhao, Daoli Zhu, Xiao Li
Date: 2021-09-29 00:00:00

The large-scale  linearly constrained nonsmooth nonconvex optimization finds wide applications in machine learning, including non-PSD Kernel SVM, linearly constrained Lasso with nonsmooth nonconvex penalty, etc.  To tackle this class of optimization problems, we propose an efficient algorithm called Nonconvex Randomized Primal-Dual Coordinate (N-RPDC) method. At each iteration, this method only randomly selects a block of primal variables to update rather than updating all the variables, which is suitable for large-scale problems.  We provide two types of convergence results for N-RPDC. We first show that any cluster point of the sequence of iterates generated by N-RPDC is almost surely (i.e., with probability 1) a stationary point.  In addition, we also provide an almost sure asymptotic convergence rate of $O(1/\sqrt{k})$. Next, we establish the expected $O(\varepsilon^{-2})$ iteration complexity of N-RPDC in order to drive a natural stationarity measure  below $\varepsilon$ in expectation. The fundamental aspect to establishing the aforementioned convergence results is a \emph{surrogate stationarity measure} we discovered for analyzing N-RPDC. Finally, we conduct a set of experiments to show the efficacy of N-RPDC.
Lattice Quantization
Authors: Clément Metz, Thibault Allenet, Johannes Christian Thiele, Antoine Dupret, Olivier Bichler
Date: 2021-09-29 00:00:00
Tasks: Quantization

Low bit quantization of weights in increasingly large deep convolutional neural networks (DCNNs) can be critical for their implementation in memory constrained hardware systems. Post-training quantization consists in quantizing a model without retraining, which is user-friendly, fast and data frugal. In this paper, we propose LatticeQ, a new post-training weight quantization method designed for DCNNs.  Instead of the standard scalar rounding widely used in state-of-the-art quantization methods, LatticeQ uses a quantizer based on lattices - discrete algebraic structures - which we show are able to exploit the inner correlations between the model parameters. LatticeQ allows us to achieve state-of-the-art results in post-training quantization, enabling us to approach full precision accuracies for bitwidths previously not accessible to post-training quantization methods. In particular, we achieve ImageNet classification results close to full precision on the popular Resnet-18/50, with only 0.5% and 5% accuracy drop for the 4-bit weights and 3-bit weights model architectures respectively.
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation
Authors: Binghui Wang, Youqi Li, Pan Zhou
Date: 2021-09-29 00:00:00
Tasks: Graph Classification, Node Classification

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure. Existing attacks to GNNs are either under the less practical threat model where the attacker is assumed to access the GNN model parameters, or under the practical black-box threat model but consider perturbing node features that are shown to be not enough effective. In this paper, we aim to bridge this gap and consider black-box attacks to GNNs with structure perturbation. We address this challenge motivated by bandits. Specifically, we formulate our attack as an online optimization with bandit feedback. This original problem is essentially NP-hard due to the fact that perturbing the graph structure is a binary optimization problem. We then propose a bandit convex optimization based attack which is proven to be {sublinear} to the query number $T$, i.e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where $N$ is the number of nodes in the graph. Finally, we evaluate our proposed attack algorithm by conducting experiments over multiple datasets and GNN models. The experimental results on various citation graphs and image graphs show that our attack is both effective and efficient.
PERSONALIZED LAB TEST RESPONSE PREDICTION WITH KNOWLEDGE AUGMENTATION
Authors: Suman Bhoi, Mong-Li Lee, Wynne Hsu, Hao Sen Andrew Fang, Ngiap Chuan Tan
Date: 2021-09-29 00:00:00
Tasks: Prediction

Personalized medical systems are rapidly gaining traction as opposed to “one size
fits all” systems. The ability to predict patients’ lab test responses and provide justification for the predictions would serve as an important decision support tool and
help clinicians tailor treatment regimes for patients. This requires one to model
the complex interactions among different medications, diseases, and lab tests. We
also need to learn a strong patient representation, capturing both the sequential
information accumulated over the visits and information from other similar patients. Further, we model the drug-lab interactions and diagnosis-lab interactions
in the form of graphs and design a knowledge-augmented approach to predict patients’ response to a target lab result. We also take into consideration patients'
past lab responses to personalize the prediction. Experiments on the benchmark
MIMIC-III and a real-world outpatient dataset demonstrate the effectiveness of
the proposed solution in reducing prediction errors by a significant margin. Case
studies show that the identified top factors for influencing the predicted lab results
are consistent with the clinicians' understanding.
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
Authors: Ziyi Chen, Shaocong Ma, Yi Zhou
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Two-player zero-sum Markov game is a fundamental problem in reinforcement learning and game theory. Although many algorithms have been proposed for solving zero-sum Markov games in the existing literature, they generally lack the desired and important features such as model-free, provably convergent, sample efficient, symmetric and private policy updates, etc. In this paper, we develop a fully decentralized stochastic policy extragradient algorithm with all these properties for solving zero-sum Markov games. In particular, our algorithm introduces multiple stochastic estimators to accurately estimate the value functions involved in the stochastic updates, and leverages entropy regularization to accelerate the convergence. Specifically, with a proper entropy-regularization parameter, we prove that the stochastic policy extragradient algorithm has a sample complexity of the order $\mathcal{O}(\frac{t_{\text{mix}}A_{\max}}{\mu_{\text{min}}\epsilon^{5.5}(1-\gamma)^{13.5}})$ for finding a solution that achieves $\epsilon$-Nash equilibrium duality gap. Such a sample complexity result substantially improves the state-of-the-art complexity results.
Planning in Stochastic Environments with a Learned Model
Authors: Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, David Silver
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Board Games, Game of Go, model, Model-based Reinforcement Learning, Playing the Game of 2048

Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is exemplified by MuZero, which has achieved state-of-the-art performance in a wide range of domains, from board games to visually rich environments, with discrete and continuous action spaces, in online and offline settings. However, previous instantiations of this approach were limited to the use of deterministic models. This limits their performance in environments that are inherently stochastic, partially observed, or so large and complex that they appear stochastic to a finite agent. In this paper we extend this approach to learn and plan with stochastic models. Specifically, we introduce a new algorithm, Stochastic MuZero, that learns a stochastic model incorporating afterstates, and uses this model to perform a stochastic tree search. Stochastic MuZero matched or exceeded the state of the art in a set of canonical single and multi-agent environments, including 2048 and backgammon, while maintaining the same performance as standard MuZero in the game of Go.
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks
Authors: Linh-Tam Tran, A F M Shahab Uddin, Sung-Ho Bae
Date: 2021-09-29 00:00:00
Tasks: Neural Architecture Search

Neural Architecture Search (NAS) is one of the most rapidly growing research fields in machine learning due to its ability to discover high-performance architectures automatically. Although conventional NAS algorithms focus on improving search efficiency (e.g., high performance with less search time), they often require a lot of memory footprint and power consumption. To remedy this problem, we propose a new paradigm for NAS that effectively reduces the use of memory while maintaining high performance. The proposed algorithm is motivated by our observation that manually designed and NAS-based architectures share similar low-level representations, regardless of the difference in the network's topology. Reflecting this, we propose a new architectural paradigm for NAS, called $\textbf{Transfer-NAS}$, that replaces several first cells in the generated architecture with conventional (hand-crafted) pre-trained blocks. As the replaced pre-trained blocks are kept frozen during training, the memory footprint can significantly be reduced. We demonstrate the effectiveness of the proposed method by incorporating it into Regularized Evolution and Differentiable ARchiTecture Search with Perturbation-based architecture selection (DARTS+PT) on NAS-Bench-201 and DARTS search spaces. Extensive experiments show that Transfer-NAS significantly decreases the memory usage up-to $\textbf{50\%}$ while achieving higher/comparable performance compared to the baselines. Furthermore, the proposed method is $\textbf{1.98$\times$}$ faster in terms of search time when incorporated to DARTS+PT on NAS-Bench-201 compared to the conventional method.
Graph Convolutional Networks via Adaptive Filter Banks
Authors: Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
Date: 2021-09-29 00:00:00
Tasks: Representation Learning

Graph convolutional networks have been a powerful tool in representation learning of networked data. However, most architectures of message passing graph convolutional networks (MPGCNs) are limited as they employ a single message passing strategy and typically focus on low-frequency information,  especially when graph features or signals are heterogeneous in different dimensions.  Then, existing spectral graph convolutional operators lack a proper sharing scheme between filters, which may result in overfitting problems with numerous parameters. In this paper, we present a novel graph convolution operator, termed BankGCN, which extends the capabilities of MPGCNs beyond single `low-pass' features and simplifies spectral methods with a carefully designed sharing scheme between filters. BankGCN decomposes multi-channel signals on arbitrary graphs into subspaces and shares adaptive filters to represent information in each subspace. The filters of all subspaces differ in frequency response and together form a filter bank. The filter bank and the signal decomposition permit to adaptively capture diverse spectral characteristics of graph data for target applications with a compact architecture. We finally show through extensive experiments that BankGCN achieves excellent performance on a collection of benchmark graph datasets.
Predicting subscriber usage: Analyzing multi-dimensional time-series using Convolutional Neural Networks
Authors: Benjamin Azaria, Lee-Ad Gottlieb
Date: 2021-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

Companies operating under the subscription model typically invest significant resources attempting to predict customer's feature usage. These predictions can be used to fuel growth: It may allow these companies to target individual customers -- for example to convert non-paying consumers to begin paying for for enhanced services -- or to identify customers not maximizing their subscription product.
This assistance can avoid an increase in the churn rate, and for some consumers may increase their usage.

In this work, we develop a deep learning model to predict the product usage of a given consumer, based on historical usage. We adapt a Convolutional Neural Network to time-series data followed by Auxiliary Output, and demonstrate that this enhanced model effectively predicts future change in usage.
Distributed Optimal Margin Distribution Machine
Authors: Yilin Wang, Nan Cao, Teng Zhang, Hai Jin
Date: 2021-09-29 00:00:00

Optimal margin Distribution Machine (ODM), a newly proposed statistical learning framework rooting in the novel margin theory, demonstrates better generalization performance than the traditional large margin based counterparts. Nonetheless, the same with other kernel methods, it suffers from the ubiquitous scalability problem in terms of both computation time and memory. In this paper, we propose a Distributed solver for ODM (DiODM), which leads to nearly ten times speedup for training kernel ODM. It exploits a novel data partition method to make the local ODM trained on each partition has a solution close to the global one. When linear kernel used, we extend a communication efficient distributed SVRG method to further accelerate the training. Extensive empirical studies validate the superiority of our proposed method compared to other off-the-shelf distributed quadratic programming solvers for kernel methods.