nils-herrmann/clean_scirepeval huggingface dataset:
Evolutionary Expectation Maximization for Generative Models with Binary Latents
Authors: Enrico Guiraud, Jakob Drefs, Joerg Luecke

We establish a theoretical link between evolutionary algorithms and variational parameter optimization of probabilistic generative models with binary hidden variables.
While the novel approach is independent of the actual generative model, here we use two such models to investigate its applicability and scalability: a noisy-OR Bayes Net (as a standard example of binary data) and Binary Sparse Coding (as a model for continuous data).

Learning of probabilistic generative models is first formulated as approximate maximum likelihood optimization using variational expectation maximization (EM).
We choose truncated posteriors as variational distributions in which discrete latent states serve as variational parameters. In the variational E-step,
the latent states are then  
optimized according to a tractable free-energy objective. Given a data point, we can show that evolutionary algorithms can be used for the variational optimization loop by (A)~considering the bit-vectors of the latent states as genomes of individuals, and by (B)~defining the fitness of the
individuals as the (log) joint probabilities given by the used generative model.

As a proof of concept, we apply the novel evolutionary EM approach to the optimization of the parameters of noisy-OR Bayes nets and binary sparse coding on artificial and real data (natural image patches). Using point mutations and single-point cross-over for the evolutionary algorithm, we find that scalable variational EM algorithms are obtained which efficiently improve the data likelihood. In general we believe that, with the link established here, standard as well as recent results in the field of evolutionary optimization can be leveraged to address the difficult problem of parameter optimization in generative models.
Gaussian Process Neurons
Authors: Sebastian Urban, Patrick van der Smagt

We propose a method to learn stochastic activation functions for use in probabilistic neural networks.
First, we develop a framework to embed stochastic activation functions based on Gaussian processes in probabilistic neural networks.
Second, we analytically derive expressions for the propagation of means and covariances in such a network, thus allowing for an efficient implementation and training without the need for sampling.
Third, we show how to apply variational Bayesian inference to regularize and efficiently train this model.
The resulting model can deal with uncertain inputs and implicitly provides an estimate of the confidence of its predictions.
Like a conventional neural network it can scale to datasets of arbitrary size and be extended with convolutional and recurrent connections, if desired.
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
Authors: Hangyu Mao, Zhibo Gong, Zhen Xiao

In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.

In this paper, we study reward design problem in cooperative MARL based on packet routing environments. Firstly, we show that the above two reward signals are prone to produce suboptimal policies. Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies. Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments. Other reward signals are also discussed in this paper. As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level
Authors: Rishi Sonthalia, Raj Rao Nadakuditi

When training a denoising neural network, we show that more data isn’t more beneficial. In fact the generalization error versus number of of training data points is a double descent curve.
Training a network to denoise noisy inputs is the most widely used technique for pre-training deep neural networks. Hence one important question is the effect of scaling the number of training data points. We formalize the question of how many data points should be used by looking at the generalization error for denoising noisy test data. Prior work on computing the generalization error focus on adding noise to target outputs. However, adding noise to the input is more in line with current pre-training practices. In the linear (in the inputs) regime, we provide an asymptotically exact formula for the generalization error for rank 1 data and an approximation for the generalization error for rank r data. We show using our formulas, that the generalization error versus number of data points follows a double descent curve. From this, we derive a formula for the amount of noise that needs to be added to the training data to minimize the denoising error and see that this follows a double descent curve as well.
Address2vec: Generating vector embeddings for blockchain analytics
Authors: Ali Hussein, Samiiha Nalwooga

Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority. All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security it’s original structure does not allow for direct analysis of address transactions. 
Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate. We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms. We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.
Do GANs learn the distribution? Some Theory and Empirics
Authors: Sanjeev Arora, Andrej Risteski, Yi Zhang

Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al. (2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al. (2017) raised doubts whether the same holds when discriminator has bounded size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support. In other words, the training objective is unable to prevent mode collapse. The current paper makes two contributions. (1) It proposes a novel test for estimating support size using the birthday paradox of discrete probability. Using this  evidence is presented that well-known GANs approaches do learn distributions of fairly low support.  (2) It theoretically studies encoder-decoder GANs architectures (e.g., BiGAN/ALI), which were proposed to learn more meaningful features via GANs, and consequently to also solve the mode-collapse issue. Our result shows that such encoder-decoder training objectives also cannot guarantee learning of the full distribution because they cannot prevent serious mode collapse. More seriously, they cannot prevent learning meaningless codes for data, contrary to usual intuition.
Holographic and other Point Set Distances for Machine Learning
Authors: Lukas Balles, Thomas Fischbacher

We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.
The NTK Adversary: An Approach to Adversarial Attacks without any Model Access
Authors: Nikolaos Tsilivis, Julia Kempe

Adversarial attacks carefully perturb natural inputs, so that a machine learning algorithm produces erroneous decisions on them. Most successful attacks on neural networks exploit gradient information of the model (either directly or by estimating it through querying the model). Harnessing recent advances in Deep Learning theory, we propose a radically different attack that eliminates that need. In particular, in the regime where the Neural Tangent Kernel theory holds, we derive a simple, but powerful strategy for attacking models, which in contrast to prior work, does not require any access to the model under attack, or any trained replica of it for that matter. Instead, we leverage the explicit description afforded by the NTK to maximally perturb the output of the model, using solely information about the model structure and the training data. We experimentally verify the efficacy of our approach, first on models that lie close to the theoretical assumptions (large width, proper initialization, etc.) and, further, on more practical scenarios, with those assumptions relaxed. In addition,  we show that our perturbations exhibit strong transferability between models.
CAT: Compression-Aware Training for bandwidth reduction
Authors: Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson

Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving visual processing tasks. One of the major obstacles hindering the ubiquitous use of CNNs for inference is their relatively high memory bandwidth requirements, which can be a main energy consumer and throughput bottleneck in hardware accelerators. Accordingly, an efficient feature map compression method can result in substantial performance gains. Inspired by quantization-aware training approaches, we propose a compression-aware training (CAT) method that involves training the model in a way that allows better compression of feature maps during inference. Our method trains the model to achieve low-entropy feature maps, which enables efficient compression at inference time using classical transform coding methods. CAT significantly improves the state-of-the-art results reported for quantization. For example, on ResNet-34 we achieve 73.1% accuracy (0.2% degradation from the baseline)  with an average representation of only 1.79 bits per value. Reference implementation accompanies the paper.
Deep Learning with Data Privacy via Residual Perturbation
Authors: Wenqi Tao, Huaming Ling, Zuoqiang Shi, Bao Wang

Protecting data privacy in deep learning (DL) is at its urgency. Several celebrated privacy notions have been established and used for privacy-preserving DL. However, many of the existing mechanisms achieve data privacy at the cost of significant utility degradation. In this paper, we propose a stochastic differential equation principled \emph{residual perturbation} for privacy-preserving DL, which injects Gaussian noise into each residual mapping of ResNets. Theoretically, we prove that residual perturbation guarantees differential privacy (DP) and reduces the generalization gap for DL. Empirically, we show that residual perturbation outperforms the state-of-the-art DP stochastic gradient descent (DPSGD) in both membership privacy protection and maintaining the DL models' utility. For instance, in the process of training ResNet8 for the IDC dataset classification, residual perturbation obtains an accuracy of 85.7\% and protects the perfect membership privacy; in contrast, DPSGD achieves an accuracy of 82.8\% and protects worse membership privacy.
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
Authors: Chandan Kumar, Subrahmanyam Vaddi, Aishwarya Sarkar

Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially
Autonomous flying drones grabbed a lot of attention in Artificial Intelligence.
Since electronic technology is getting smaller, cheaper and more efficient, huge
advancement in the study of UAVs has been observed recently. From monitoring
floods, discerning the spread of algae in water bodies to detecting forest trail, their
application is far and wide. Our work is mainly focused on autonomous flying
drones where we establish a case study towards efficiency, robustness and accuracy
of UAVs where we showed our results well supported through experiments.
We provide details of the software and hardware architecture used in the study. We
further discuss about our implementation algorithms and present experiments that
provide a comparison between three different state-of-the-art algorithms namely
TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power
consumption and inference time. In our study, we have shown that MobileNet has
produced better results with very less computational requirement and power consumption.
We have also reported the challenges we have faced during our work
as well as a brief discussion on our future work to improve safety features and
performance.
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
Authors: Ke Li*, Shichong Peng*, Jitendra Malik

Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an alternative approach based on an extension of the method of Implicit Maximum Likelihood Estimation (IMLE). We demonstrate greater effectiveness at noise reduction and preservation of the original colours and shapes, yielding more realistic super-resolved images.
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Authors: Stephanie Ger, Diego Klabjan

We introduce a novel synthetic oversampling method for variable length, multi- feature sequence datasets based on autoencoders and generative adversarial net- works. We show that this method improves classification accuracy for highly imbalanced sequence classification tasks. We show that this method outperforms standard oversampling techniques that use techniques such as SMOTE and autoencoders. We also use generative adversarial networks on the majority class as an outlier detection method for novelty detection, with limited classification improvement. We show that the use of generative adversarial network based synthetic data improves classification model performance on a variety of sequence data sets.
Diffusion-Based Representation Learning
Authors: Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

Score-based methods represented as stochastic differential equations on a continuous time domain have recently proven successful as a non-adversarial generative model. Training such models relies on denoising score matching, which can be seen as multi-scale denoising autoencoders. Here, we augment the denoising score-matching framework to enable representation learning without any supervised signal. GANs and VAEs learn representations by directly transforming latent codes to data samples. In contrast, the introduced diffusion based representation learning relies on a new formulation of the denoising score-matching objective and thus encodes information needed for denoising. We illustrate how this difference allows for manual control of the level of details encoded in the representation. Using the same approach, we propose to learn an infinite-dimensional latent code which achieves improvements of state-of-the-art models on semi-supervised image classification. As a side contribution, we show how adversarial training in score-based models can improve sample quality and improve sampling speed using a new approximation of the prior at smaller noise scales.
Normalization Gradients are Least-squares Residuals
Authors: Yi Liu

Batch Normalization (BN) and its variants have seen widespread adoption in the deep learning community because they improve the training of deep neural networks. Discussions of why this normalization works so well remain unsettled.  We make explicit the relationship between ordinary least squares and partial derivatives computed when back-propagating through BN. We recast the back-propagation of BN as a least squares fit, which zero-centers and decorrelates partial derivatives from normalized activations. This view, which we term {\em gradient-least-squares}, is an extensible and arithmetically accurate description of BN. To further explore this perspective, we motivate, interpret, and evaluate two adjustments to BN.
Fighting Filterbubbles with Adversarial BERT-Training for News-Recommendation
Authors: Lukas Pfahler, Katharina Morik

Recommender engines play a role in the emergence and reinforcement of filter bubbles. When these systems learn that a user prefers content from a particular site, the user will be less likely to be exposed to different sources or opinions and, ultimately, is more likely to develop extremist tendencies.
We trace the roots of this phenomenon to the way the recommender engine represents news articles. The vectorial features modern systems extract from the plain text of news articles are already highly predictive of the associated news outlet. We propose a new training scheme based on adversarial machine learning to tackle this issue . Our  experiments show that the features we can extract this way are significantly less predictive of the news outlet and thus offer the possibility to reduce the risk of manifestation of new filter bubbles. We validate our intuitions in a news recommendation task using a recent attention-based recommendation system.
Stagnant zone segmentation with U-net
Authors: Selam Waktola, Laurent Babout, Krzysztof Grudzien

Silo discharging and monitoring the process for
industrial or research application depend on computerized
segmentation of different parts of images such as stagnant and
flowing zones which is the toughest task. X-ray Computed
Tomography (CT) is one of a powerful non-destructive technique
for cross-sectional images of a 3D object based on X-ray
absorption. CT is the most proficient for investigating different
granular flow phenomena and segmentation of the stagnant zone
as compared to other imaging techniques. In any case, manual
segmentation is tiresome and erroneous for further investigations.
Hence, automatic and precise strategies are required. In the
present work, a U-net architecture is used for segmenting the
stagnant zone during silo discharging process. This proposed
image segmentation method provides fast and effective outcomes
by exploiting a convolutional neural networks technique with an
accuracy of 97 percent
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Authors: Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille

Contrastive learning has recently been a core for unsupervised visual representation learning. Without human annotation, the common practice is to perform an instance discrimination task: Given a query image crop, label crops from the same image as positives, and crops from other randomly sampled images as negatives. An important limitation of this label assignment is that it can not reflect the heterogeneous similarity of the query crop to crops from other images, but regarding them as equally negative. To address this issue, inspired by consistency regularization in semi-supervised learning, we propose Consistent Contrast (CO2), which introduces a consistency term into unsupervised contrastive learning framework. The consistency term takes the similarity of the query crop to crops from other images as unlabeled, and the corresponding similarity of a positive crop as a pseudo label. It then encourages consistency between these two similarities. Empirically, CO2 improves Momentum Contrast (MoCo) by 2.9% top-1 accuracy on ImageNet linear protocol, 3.8% and 1.1% top-5 accuracy on 1% and 10% labeled semi-supervised settings. It also transfers to image classification, object detection, and semantic segmentation on PASCAL VOC. This shows that CO2 learns better visual representations for downstream tasks.
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
Authors: Xiang Gao, Wei Hu, Guo-Jun Qi

We present the Topology Transformation Equivariant Representation (TopoTER) learning, a general paradigm of unsupervised learning of node representations of graph data for the wide applicability to Graph Convolutional Neural Networks (GCNNs). We formalize the TopoTER from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations. We derive that maximizing such mutual information can be relaxed to minimizing the cross entropy between the applied topology transformation and its estimation from node representations. In particular, we seek to sample a subset of node pairs from the original graph and flip the edge connectivity between each pair to transform the graph topology. Then, we self-train a representation encoder to learn node representations by reconstructing the topology transformations from the feature representations of the original and transformed graphs. In experiments, we apply the TopoTER to the downstream node and graph classification tasks, and results show that the TopoTER outperforms the state-of-the-art unsupervised approaches.
DNBP: Differentiable Nonparametric Belief Propagation
Authors: Anthony Opipari, Jana Pavlasek, Chao Chen, Shoutian Wang, Karthik Desingh, Odest Jenkins

We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model. In this work, we replace each crafted factor with a differentiable neural network enabling the factors to be learned using an efficient optimization routine from labeled data. By combining differentiable neural networks with an efficient belief propagation algorithm, our method learns to maintain a set of marginal posterior samples using end-to-end training. We evaluate our differentiable nonparametric belief propagation (DNBP) method on a set of articulated pose tracking tasks and compare performance with learned baselines. Results from these experiments demonstrate the effectiveness of using learned factors for tracking and suggest the practical advantage over hand-crafted approaches. The project webpage is available at: https://sites.google.com/view/diff-nbp
Sequence to Sequence Transduction with Hard Monotonic Attention
Authors: Roee Aharoni, Yoav Goldberg

We present a supervised sequence to sequence transduction model with a hard attention mechanism which combines the more traditional statistical alignment methods with the power of recurrent neural networks. We evaluate the model on the task of morphological inflection generation and show that it provides state of the art results in various setups compared to the previous neural and non-neural approaches. Eventually we present an analysis of the learned representations for both hard and soft attention models, shedding light on the features such models extract in order to solve the task.