pwc-archive/papers-with-abstracts huggingface dataset:
CaLFADS: latent factor analysis of dynamical systems in calcium imaging data
Authors: Luke Yuri Prince, Shahab Bakhtiari, Colleen J Gillon, Blake Aaron Richards
Date: 2021-01-01 00:00:00
Tasks: Point Processes

Dynamic latent variable modelling has been a hugely powerful tool in understanding how spiking activity in populations of neurons can perform computations necessary for adaptive behaviour. The success of such approaches has been enabled by the ability to construct models derived with the characterization of spiking activity as point-processes since spiking dynamics occur on a much faster time-scale than the computational dynamics being inferred. Other experimental techniques, such as calcium imaging, pose a problem for latent variable modelling of computational dynamics, since the time-scales of calcium dynamics and computational dynamics overlap. As such, the success of dynamic latent variable modelling in calcium imaging data rests on being able to disentangle the contribution of these two sources of variation. Here we extend recent advances using variational autoencoders to analyze neural data, by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using built-in inductive biases for calcium dynamics, we can capture calcium flux as well as underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly infer an underlying Lorenz attractor at the same time as calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data, thereby providing the neuroscience community with a new analysis tool for a wider array of data modalities.
Importance and Coherence: Methods for Evaluating Modularity in Neural Networks
Authors: Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell
Date: 2021-01-01 00:00:00
Tasks: Clustering

As deep neural networks become more advanced and widely-used, it is important to understand their inner workings. Toward this goal, modular interpretations are appealing because they offer flexible levels of abstraction aside from standard architectural building blocks (e.g., neurons, channels, layers). In this paper, we consider the problem of assessing how functionally interpretable a given partitioning of neurons is. We propose two proxies for this: importance which reflects how crucial sets of neurons are to network performance, and coherence which reflects how consistently their neurons associate with input/output features. To measure these proxies, we develop a set of statistical methods based on techniques that have conventionally been used for the interpretation of individual neurons. We apply these methods on partitionings generated by a spectral clustering algorithm which uses a graph representation of the network's neurons and weights. We show that despite our partitioning algorithm using neither activations nor gradients, it reveals clusters with a surprising amount of importance and coherence. Together, these results support the use of modular interpretations, and graph-based partitionings in particular, for interpretability.
Rapid Neural Pruning for Novel Datasets with Set-based Task-Adaptive Meta-Pruning
Authors: Minyoung Song, Jaehong Yoon, Eunho Yang, Sung Ju Hwang
Date: 2021-01-01 00:00:00
Tasks: Cloud Computing, Network Pruning

As deep neural networks are growing in size and being increasingly deployed to more resource-limited devices, there has been a recent surge of interest in network pruning methods, which aim to remove less important weights or activations of a given network. A common limitation of most existing pruning techniques, is that they require pre-training of the networks at least once before pruning, and thus we can benefit from reduction in memory and computation only at the inference time. However, reducing the training cost of neural networks with rapid structural pruning may be beneficial either to minimize monetary cost with cloud computing or to enable on-device learning on a resource-limited device. Recently introduced random-weight pruning approaches can eliminate the needs of pretraining, but they often obtain suboptimal performance over conventional pruning techniques and also does not allow for faster training since they perform unstructured pruning. To overcome their limitations, we propose Set-based Task-Adaptive Meta Pruning (STAMP), which task-adaptively prunes a network pretrained on a large reference dataset by generating a pruning mask on it as a function of the target dataset. To ensure maximum performance improvements on the target task, we meta-learn the mask generator over different subsets of the reference dataset, such that it can generalize well to any unseen datasets within a few gradient steps of training. We validate STAMP against recent advanced pruning methods on benchmark datasets, on which it not only obtains significantly improved compression rates over the baselines at similar accuracy, but also orders of magnitude faster training speed.
On the cost of homogeneous network building blocks and parameter sharing
Authors: Thomas Pfeil
Date: 2021-01-01 00:00:00
Tasks: Semantic Segmentation

Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. To this end, common requirements are low latency, high accuracy and a small memory footprint. A good trade-off between latency and accuracy has been shown with networks comprising multiple outputs at different depths. In this study, we further optimize such networks for efficient inference by introducing a homogeneous network structure to compute these outputs. A single building block is iteratively executed such that its output is refined over the iterations. On the CamVid and Cityscapes datasets for semantic segmentation, we show and compare empirical results for two scenarios: in the first scenario, the parameters are allowed to be updated at every iteration, whereas in the second scenario, parameters are kept constant and are shared between iterations. We compensate for both the homogeneity of the network architecture and the weight sharing by increasing the number of multiply-accumulate operations by a factor of 3, respectively. However, the potentially more efficient implementation of the introduced networks on novel, massively-parallel hardware accelerators may outweigh the increased number of operations.
The Negative Pretraining Effect in Sequential Deep Learning and Three Ways to Fix It
Authors: Julian G. Zilly, Franziska Eckert, Bhairav Mehta, Andrea Censi, Emilio Frazzoli
Date: 2021-01-01 00:00:00

Negative pretraining is a prominent sequential learning effect of neural networks where a pretrained model obtains a worse generalization performance than a model that is trained from scratch when either are trained on a target task. We conceptualize the ingredients of this problem setting and examine the negative pretraining effect experimentally by providing three interventions to remove and fix it. First, acting on the learning process, altering the learning rate after pretraining can yield even better results than training directly on the target task. Second, on the learning task-level, we intervene by increasing the discretization of data distribution changes from start to target task instead of “jumping” to a target task. Finally at the model-level, resetting network biases to larger values likewise removes negative pretraining effects, albeit to a smaller degree. With these intervention experiments, we aim to provide new evidence to help understand the subtle influences that neural network training and pretraining can have on final generalization performance on a target task in the context of negative pretraining.
Recursive Neighborhood Pooling for Graph Representation Learning
Authors: Behrooz Tahmasebi, Stefanie Jegelka
Date: 2021-01-01 00:00:00
Tasks: Graph Representation Learning, Representation Learning

 While Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in their expressive power. In response, several higher-order GNNs have been proposed, which substantially increase the expressive power, but at a large computational cost. Motivated by this gap, we introduce and analyze a new recursive pooling technique of local neighborhoods that allows different tradeoffs of computational cost and expressive power. First, we show that this model can count subgraphs of size $k$, and thereby overcomes a known limitation of low-order GNNs. Second, we prove that, in several cases, RNP-GNNs can greatly reduce computational complexity compared to the existing higher-order $k$-GNN and Local Relational Pooling (LRP) networks.
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness
Authors: Yufeng Zhang, Yunan Zhang, ChengXiang Zhai
Date: 2021-01-01 00:00:00
Tasks: Adversarial Robustness, counterfactual

To classify images, neural networks extract features from raw inputs and then sum them up with fixed weights via the fully connected layer. However, the weights are fixed despite the input types. Such fixed prior limits networks' flexibility in adjusting feature reliance, which in turn enables attackers to flip networks' predictions by corrupting the most brittle features whose value would change drastically by minor perturbations. Inspired by the analysis, we replace the original fixed fully connected layer by dynamically calculating the posterior weight for each feature according to the input and connections between them. Also, a counterfactual baseline is integrated to precisely characterize the credit of each feature's contribution to the robustness and generality of the model. We empirically demonstrate that the proposed algorithm improves both standard and robust error against several strong attacks across various major benchmarks.  Finally, we theoretically prove the minimal structure requirement for our framework to improve adversarial robustness in a fairly simple and natural setting.
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets
Authors: SeungHyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin
Date: 2021-01-01 00:00:00
Tasks: D4RL, MuJoCo, Offline RL, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent progress in offline reinforcement learning (RL) has made it possible to train strong RL agents from previously-collected, static datasets. However, depending on the quality of the trained agents and the application being considered, it is often desirable to improve such offline RL agents with further online interaction. As it turns out, fine-tuning offline RL agents is a non-trivial challenge, due to distribution shift – the agent encounters out-of-distribution samples during online interaction, which may cause bootstrapping error in Q-learning and instability during fine-tuning. In order to address the issue, we present a simple yet effective framework, which incorporates a balanced replay scheme and an ensemble distillation scheme. First, we propose to keep separate offline and online replay buffers, and carefully balance the number of samples from each buffer during updates. By utilizing samples from a wider distribution, i.e., both online and offline samples, we stabilize the Q-learning. Next, we present an ensemble distillation scheme, where we train an ensemble of independent actor-critic agents, then distill the policies into a single policy. In turn, we improve the policy using the Q-ensemble during fine-tuning, which allows the policy updates to be more robust to error in each individual Q-function. We demonstrate the superiority of our method on MuJoCo datasets from the recently proposed D4RL benchmark suite.
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method
Authors: Lingjun Dai, Qingtian Zhang, Huaqiang Wu
Date: 2021-01-01 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Quantization, Semantic Segmentation

Crossbar-enabled analog computing-in-memory (CACIM) systems can significantly improve the computation speed and energy efficiency of deep neural networks (DNNs). However, the transition of DNN from the digital system to CACIM system usually reduces its accuracy. The major issue is that the weights of DNN are stored and calculated directly on analog quantities in CACIM system. The variation and programming overhead of the analog weight limit the precision.
Therefore, a suitable quantization algorithm is important when deploying a DNN into CACIM systems to obtain less accuracy loss. The analog weight has its unique advantages when doing quantization. Because there is no encoding and decoding process, the range of quantization function will not affect the computing process. Therefore, a generalized quantization method which does not constrain the range of quanta and can obtain less quantization error will be effective in CACIM system. For the first time, we introduced a generalized quantization method into CACIM systems and showed the superior performance on a series of computer vision tasks, such as image classification, object detection, and semantic segmentation. Using the generalized quantization method, the DNN with 8-level analog weights can outperform the 32-bit networks. With fewer levels, the generalized quantization method can obtain less accuracy loss than other uniform quantization methods.
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design
Authors: Tim Kucera, Karsten Michael Borgwardt, Matteo Togninalli, Laetitia Papaxanthos
Date: 2021-01-01 00:00:00
Tasks: Protein Design, valid

The availability of vast protein sequence information and rich functional annotations thereof has a large potential for protein design applications in biomedicine and synthetic biology. To this date, there exists no method for the general-purpose design of proteins without any prior knowledge about the protein of interest, such as costly and rare structure information or sequence fragments. However, the Gene Ontology (GO) database provides information about the hierarchical organisation of protein functions, and thus could inform generative models about the underlying complex sequence-function relationships, replacing the need for structural data. We therefore propose to use conditional generative adversarial networks (cGANs) on the task of fast \emph{de novo} hierarchical multi-label protein design. We generate valid protein sequences exhibiting a large set of molecular functions extracted from the GO database, using a single model and without any prior information. We shed light on efficient conditioning mechanisms and adapted network architectures thanks to a thorough hyperparameter selection process and analysis. We further provide statistically- and biologically-driven evaluation measures for generative models in the context of protein design to assess the quality of the generated sequences and facilitate progress in the field. We show that our proposed model, ProteoGAN, outperforms several baselines when designing proteins given a functional label and generates well-formed sequences.
Sequence Metric Learning as Synchronization of Recurrent Neural Networks
Authors: Paul Compagnon, Grégoire Lefebvre, Stefan Duffner, Christophe Garcia
Date: 2021-01-01 00:00:00
Tasks: Activity Recognition, Metric Learning, Representation Learning

Sequence metric learning is becoming a widely adopted approach for various applications dealing with sequential multi-variate data such as activity recognition or natural language processing and is most of the time tackled with sequence alignment approaches or representation learning. 
In this paper, we propose to study this subject from the point of view of dynamical system theory by drawing the analogy between synchronized trajectories produced by dynamical systems and the distance between similar sequences processed by a siamese recurrent neural network. 
Indeed, a siamese recurrent network comprises two identical sub-networks, two identical dynamical systems which can theoretically achieve complete synchronization if a coupling is introduced between them. 
We therefore propose a new neural network model that implements this coupling with a new gate integrated into the classical Gated Recurrent Unit architecture. This model is thus able to simultaneously learn a similarity metric and the synchronization of unaligned multi-variate sequences in a weakly supervised way.
Our experiments show that introducing such a coupling improves the performance of the siamese Gated Recurrent Unit architecture on an activity recognition dataset.
Time Series Counterfactual Inference with Hidden Confounders
Authors: Guangyu Li, Jiahao Chen, Samuel A Assefa, Yan Liu
Date: 2021-01-01 00:00:00
Tasks: counterfactual, Counterfactual Inference, Gaussian Processes, Time Series, Time Series Analysis

We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications. ACODEs model interventions in continuous time with differential equations, augmented by auxiliary confounding variables to reduce inference bias. Experiments on tumor growth simulation and sepsis patient treatment response show that ACODEs outperform other methods like counterfactual Gaussian processes, recurrent marginal structural networks, and time series deconfounders in the accuracy of counterfactual inference. The learned auxiliary variables also reveal new insights into causal interventions and hidden confounders.
Latent Convergent Cross Mapping
Authors: Edward De Brouwer, Adam Arany, Jaak Simm, Yves Moreau
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Causal Inference, Time Series, Time Series Analysis

Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems. 
However, accurately inferring causal structures within a phenomenon based on observational data only is still an open problem. Indeed, this type of data usually consists in short time series with missing or noisy values for which causal inference is increasingly difficult. In this work, we propose a method to uncover causal relations between chaotic dynamical systems from short, noisy and sporadic time series (that is, incomplete observations at infrequent and irregular intervals) where the classical convergent cross mapping (CCM) fails. Our method works by learning a Neural ODE latent process modeling the state-space dynamics of the time series and by checking the existence of a continuous map between the resulting processes. We provide theoretical analysis and show empirically that Latent-CCM can reliably uncover the true causal pattern, unlike traditional methods.
Unsupervised Active Pre-Training for Reinforcement Learning
Authors: Hao liu, Pieter Abbeel
Date: 2021-01-01 00:00:00
Tasks: Atari Games, Contrastive Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unsupervised Pre-training

We introduce a new unsupervised pre-training method for reinforcement learning called $\textbf{APT}$, which stands for $\textbf{A}\text{ctive}\textbf{P}\text{re-}\textbf{T}\text{raining}$. APT learns a representation and a policy initialization by actively searching for novel states in reward-free environments. We use the contrastive learning framework for learning the representation from collected transitions. The key novel idea is to collect data during pre-training by maximizing a particle based entropy computed in the learned latent representation space. By doing particle based entropy maximization, we alleviate the need for challenging density modeling and are thus able to scale our approach to image observations. APT successfully learns meaningful representations as well as policy initializations without using any reward. APT is conceptually simple to implement, scalable, and empirically powerful. We empirically evaluate APT on the Atari game suite and DMControl suite by exposing task-specific reward to agent after a long unsupervised pre-training phase. On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult for training from scratch. On Atari games, APT achieves human-level performance on $12$ games and obtains highly competitive performance compared to canonical RL algorithms.
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents
Authors: Ezgi Korkmaz, Henrik Sandberg, Gyorgy Dan
Date: 2021-01-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adversarial attacks against deep neural networks have been widely studied. Adversarial examples for deep reinforcement learning (DeepRL) have significant security implications, due to the deployment of these algorithms in many application domains. In this work we formalize an optimal myopic adversary for deep reinforcement learning agents. Our adversary attempts to find a bounded perturbation of the state which minimizes the value of the action taken by the agent. We show with experiments in various games in the Atari environment that our attack formulation achieves significantly larger impact as compared to the current  state-of-the-art. Furthermore, this enables us to lower the bounds by several orders of magnitude on the perturbation needed to efficiently achieve significant impacts on DeepRL agents.
On Self-Supervised Image Representations for GAN Evaluation
Authors: Stanislav Morozov, Andrey Voynov, Artem Babenko
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Contrastive Learning, General Classification

The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature.

In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading.