nils-herrmann/clean_scirepeval huggingface dataset:
Total Style Transfer with a Single Feed-Forward Network
Authors: Minseong Kim, Hyun-Chul Choi

Recent image style transferring methods achieved arbitrary stylization with input content and style images. To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale. However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks. This shortcoming resulted in generating partially and inexactly transferred style in the generated images.
To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process. First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps. Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection. Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.
Deep automodulators
Authors: Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin

We introduce a novel autoencoder model that deviates from traditional autoencoders by using the full latent vector to independently modulate each layer in the decoder. We demonstrate how such an 'automodulator' allows for a principled approach to enforce latent space disentanglement, mixing of latent codes, and a straightforward way to utilize prior information that can be construed as a scale-specific invariance. Unlike GANs, autoencoder models can directly operate on new real input samples. This makes our model directly suitable for applications involving real-world inputs. As the architectural backbone, we extend recent generative autoencoder models that retain input identity and image sharpness at high resolutions better than VAEs. We show that our model achieves state-of-the-art latent space disentanglement and achieves high quality and diversity of output samples, as well as faithfulness of reconstructions.
SHAMANN: Shared Memory Augmented Neural Networks
Authors: Cosmin I. Bercea, Olivier Pauly, Andreas K. Maier, Florin C. Ghesu

Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping. While these methods effectively exploit global image context, the learning and computational complexities are high. We propose shared memory augmented neural network actors as a dynamically scalable alternative. Based on a decomposition of the image into a sequence of local patches, we train such actors to sequentially segment each patch. To further increase the robustness and better capture shape priors, an external memory module is shared between different actors, providing an implicit mechanism for image information exchange. Finally, the patch-wise predictions are aggregated to a complete segmentation mask. We demonstrate the benefits of the new paradigm on a challenging lung segmentation problem based on chest X-Ray images, as well as on two synthetic tasks based on the MNIST dataset. On the X-Ray data, our method achieves state-of-the-art accuracy with a significantly reduced model size compared to reference methods. In addition, we reduce the number of failure cases by at least half.
ADIS-GAN: Affine Disentangled GAN
Authors: Letao Liu, Martin Saerbeck, Justin Dauwels

This paper proposes Affine Disentangled GAN (ADIS-GAN), which is a Generative Adversarial Network that can explicitly disentangle affine transformations in a self-supervised and rigorous manner.  The objective is inspired by InfoGAN, where an additional affine regularizer acts as the inductive bias.  The affine regularizer is rooted in the affine transformation properties of images, changing some properties of the underlying images, while leaving all other properties invariant. We derive the affine regularizer by decomposing the affine matrix into separate transformation matrices and inferring the transformation parameters by maximum-likelihood estimation.  Unlike the disentangled representations learned by existing approaches, the features learned by ADIS-GAN are axis-aligned and scalable, where transformations such as rotation, horizontal and vertical zoom, horizontal and vertical skew,  horizontal and vertical translation can be explicitly selected and learned. ADIS-GAN successfully disentangles these features on the MNIST, CelebA, and dSprites datasets.
A Bilingual Generative Transformer for Semantic Sentence Embedding
Authors: John Wieting, Graham Neubig, Taylor Berg-Kirkpatrick

Semantic sentence embedding models take natural language sentences and turn them into vectors, such that similar vectors indicate similarity in the semantics between the sentences. Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific. We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. Our proposed approach differs from past work on semantic sentence encoding in two ways. First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model’s posterior to predict sentence embeddings for monolingual data at test time. Second, we use high- capacity transformers as both data generating distributions and inference networks – contrasting with most past work on sentence embeddings. In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of se- mantic similarity evaluations. Further, we demonstrate that our approach yields the largest gains on more difficult subsets of test where simple word overlap is not a good indicator of similarity.
Causal Inference Q-Network: Toward Resilient Reinforcement Learning
Authors: Chao-Han Huck Yang, Danny I-Te Hung, Yi Ouyang, Pin-Yu Chen

Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive faulty observation by abrupt interferences such as black-out, frozen-screen, and adversarial perturbation. How to design a resilient DRL algorithm against these rare but mission-critical and safety-crucial scenarios is an important yet challenging task. In this paper, we consider a resilient DRL framework with observational interferences. Under this framework, we discuss the importance of the causal relation and propose a causal inference based DRL algorithm called causal inference Q-network (CIQ). We evaluate the performance of CIQ in several benchmark DRL environments with different types of interferences. Our experimental results show that the proposed CIQ method could achieve higher performance and more resilience against observational interferences.
Deep Graph Translation
Authors: Xiaojie Guo, Lingfei Wu, Liang Zhao

Deep graph generation models have achieved great successes recently, among which, however, are typically unconditioned generative models that have no control over the target graphs are given an input graph. In this paper, we propose a novel Graph-Translation-Generative-Adversarial-Networks (GT-GAN) that transforms the input graphs into their target output graphs. GT-GAN consists of a graph translator equipped with innovative graph convolution and deconvolution layers to learn the translation mapping considering both global and local features, and a new conditional graph discriminator to classify target graphs by conditioning on input graphs. Extensive experiments on multiple synthetic and real-world datasets demonstrate that our proposed GT-GAN significantly outperforms other baseline methods in terms of both effectiveness and scalability. For instance, GT-GAN achieves at least 10X and 15X faster runtimes than GraphRNN and RandomVAE, respectively, when the size of the graph is around 50.
Unsupervised Deep Learning of State Representation Using Robotic Priors 
Authors: Timothee LESORT, David FILLIAT

Our understanding of the world depends highly on how we represent it.  Using background knowledge about its complex underlying physical rules, our brain can produce intuitive and simplified representations which it can easily use to solve problems. The approach of this paper aims to reproduce this simplification process using a neural network to produce a simple low dimensional state representation of the world from images acquired by a robot. As proposed in Jonschkowski &amp; Brock (2015), we train the neural network in an unsupervised way, using the "a priori" knowledge we have about the world as loss functions called "robotic priors" that we implemented through a siamese network. This approach has been used to learn a one dimension representation of a Baxter head position from raw images. The experiment resulted in a 97,7% correlation between the learned representation and the ground truth, and show that relevant visual features form the environment are learned.
TPO: TREE SEARCH POLICY OPTIMIZATION FOR CONTINUOUS ACTION SPACES
Authors: Amir Yazdanbakhsh, Ebrahim Songhori, Robert Ormandi, Anna Goldie, Azalia Mirhoseini

Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains.In this work, we introduceTPO, a tree search based policy optimization method for continuous environments. TPO takes a hybrid approach to policy optimization.  Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial. To overcome these challenges, we propose limiting tree search branching factor by drawing only few action samples from the policy distribution and define a new loss function based on the trajectories’ mean and standard deviations.  Our approach led to some non-intuitive findings.  MCTS training generally requires a large number of samples and simulations. However, we observed that bootstrappingtree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few number of simulations. Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it computationally and prohibitively expensive. In our experiments, we use PPO as our baseline policy optimization algorithm. TPO significantly improves the policy on nearly all of our benchmarks.  For example, in complex environments such as Humanoid, we achieve a 2.5×improvement over the baseline algorithm.
A Generalised Inverse Reinforcement Learning Framework
Authors: Firas Jarboui, Vianney Perchet

The global objective of inverse Reinforcement Learning (IRL) is to estimate the unknown cost function of some MDP based on observed trajectories generated by (approximate) optimal policies. The classical approach consists in tuning this cost function so that associated optimal trajectories (that minimise the cumulative discounted cost, i.e. the classical RL loss) are “similar” to the observed ones. Prior contributions focused on penalising degenerate solutions and improving algorithmic scalability. Quite orthogonally to them,  we question the pertinence of characterising optimality with respect to the cumulative discounted cost as it induces an implicit bias against policies with longer mixing times. State of the art value based RL algorithms circumvent this issue by solving for the fixed point of the Bellman optimality operator, a stronger criterion that is not well defined for the inverse problem.
To alleviate this bias in IRL, we introduce an alternative training loss that puts more weights on future states which yields a reformulation of the (maximum entropy) IRL problem. The algorithms we devised exhibit enhanced performances (and similar tractability) than off-the-shelf ones in multiple OpenAI gym environments.
Class-Weighted Evaluation Metrics for Imbalanced Data Classification
Authors: Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Balanced Accuracy is a popular metric used to evaluate a classifier’s prediction performance under such scenarios. However, this metric falls short when classes vary in importance, especially when class importance is skewed differently from class cardinality distributions. In this paper, we propose a simple and general-purpose evaluation framework for imbalanced data classification that is sensitive to arbitrary skews in class cardinalities and importances. Experiments with several state-of-the-art classifiers tested on real-world datasets and benchmarks from two different domains show that our new framework is more effective than Balanced Accuracy –- not only in evaluating and ranking model predictions, but also in training the models themselves.
A Simple Approach to Adversarial Robustness in Few-shot Image Classification 
Authors: Akshayvarun Subramanya, Hamed Pirsiavash

Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers.  We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes.  We show that standard adversarial training on base categories along with centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks such as Mini-ImageNet, CIFAR-FS and CUB datasets. Our method is simple and easy to scale, and with little effort can lead to robust few-shot classifiers.
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Authors: Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, Tom Goldstein

Tabular data underpins numerous high-impact applications of machine learning from fraud detection to genomics and healthcare.  Classical approaches to solving tabular problems, such as gradient boosting and random forests, are widely used by practitioners.  However, recent deep learning methods have achieved a degree of performance competitive with popular techniques.  We devise a hybrid deep learning approach to solving tabular data problems.  Our method, SAINT, performs attention over both rows and columns, and it includes an enhanced embedding method.  We also study a new contrastive self-supervised pre-training method for use when labels are scarce.  SAINT consistently improves performance over previous deep learning methods, and it even performs competitively with gradient boosting methods, including XGBoost, CatBoost, and LightGBM, on average over $30$ benchmark datasets in regression, binary classification, and multi-class classification tasks.
Inference-Time Personalized Federated Learning
Authors: Ohad Amosy, Gal Eyal, Gal Chechik

In Federated learning (FL), multiple clients collaborate to learn a model through a central server but keep the data decentralized. Personalized federated learning (PFL) further extends FL to handle data heterogeneity between clients by learning personalized models. In both FL and PFL,  all clients participate in the training process and their labeled data is used for training. However, in reality, novel clients may wish to join a prediction service after it has been deployed, obtaining predictions for their own unlabeled data.
 
Here, we defined a new learning setup, Inference-Time PFL (IT-PFL), where a model trained on a set of clients, needs to be later evaluated on novel unlabeled clients at inference time.  We propose a novel approach to this problem IT-PFL-HN, based on a hypernetwork module and an encoder module. Specifically, we train an encoder network that learns a representation for a client given its unlabeled data. That client representation is fed to a hypernetwork that generates a personalized model for that client. Evaluated on four benchmark datasets, we find that IT-PFL-HN generalizes better than current FL and PFL methods, especially when the novel client has a large domain shift. We also analyzed the generalization error for the novel client, showing how it can be bounded using results from multi-task learning and domain adaptation. Finally, since novel clients do not contribute their data to training, they can potentially have better control over their data privacy; Indeed, we showed analytically and experimentally how novel clients can apply differential privacy to their data.
Individuality in the hive - Learning to embed lifetime social behaviour of honey bees
Authors: Benjamin Wild, David Dormagen, Michael L Smith, Tim Landgraf

Honey bees are a popular model for complex social systems, in which global behavior emerges from the actions and interactions of thousands of individuals. While the average life of a bee is organized as a sequence of tasks roughly determined by age, there is substantial variation at the individual level. For example, young bees can become foragers early in life, depending on the colony’s needs. Using a unique dataset containing lifetime trajectories of all individuals over multiple generations in two honey bee colonies, we propose a new temporal matrix factorization model that jointly learns the average developmental path and structured variations of individuals in the social network over their entire lives. Our method yields inherently interpretable embeddings that are biologically plausible and consistent over time, which allow one to compare individuals regardless of when, or in which colony, they lived. Our method provides a quantitative framework for understanding behavioral heterogeneity in complex social systems applicable in fields such as behavioral biology, social sciences, neuroscience, and information science.
Measuring Calibration in Deep Learning
Authors: Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel, Linchuan Zhang, Dustin Tran

Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. We propose two new measures for calibration, the Static Calibration Error (SCE) and Adaptive Calibration Error (ACE). These measures take into account every prediction made by a model, in contrast to the popular Expected Calibration Error.
Generative Fairness Teaching
Authors: Rongmei Lin, Hanjun Dai, Li Xiong, Wei Wei

Increasing evidences has shown that data biases towards sensitive features such as gender or race are often inherited or even amplified by machine learning models. Recent advancements in fairness mitigate such biases by adjusting the predictions across sensitive groups during the training. Such a correction, however, can only take advantage of samples in a fixed dataset, which usually has limited amount of samples for the minority groups. We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training. We employ such a teaching strategy by implementing a Generative Fairness Teacher (GFT) that dynamically adjust the proportion of training data for a biased student model. Experimental results indicated that our teacher model is capable of guiding a wide range of biased models by improving  the fairness and performance trade-offs significantly.
Stability analysis of SGD through the normalized loss function
Authors: Alexandre Lemire Paquin, Brahim Chaib-draa, Philippe Giguère

We prove new generalization bounds for stochastic gradient descent for both the convex and non-convex cases. Our analysis is based on the stability framework. We analyze stability with respect to the normalized version of the loss function used for training. This leads to investigating a form of angle-wise stability instead of euclidean stability in weights. For neural networks, the measure of distance we consider is invariant to rescaling the weights of each layer. Furthermore, we exploit the notion of on-average stability in order to obtain a data-dependent quantity in the bound. This data-dependent quantity is seen to be more favorable when training with larger learning rates in our numerical experiments. This might help to shed some light on why larger learning rates can lead to better generalization in some practical scenarios.
Generative Hierarchical Models for Parts, Objects, and Scenes
Authors: Fei Deng, Zhuo Zhi, Sungjin Ahn

Hierarchical structure such as part-whole relationship in objects and scenes are the most inherent structure in natural scenes. Learning such representation via unsupervised learning can provide various benefits such as interpretability, compositionality, and transferability, which are important in many downstream tasks. In this paper, we propose the first hierarchical generative model for learning multiple latent part-whole relationships in a scene. During inference, taking top-down approach, our model infers the representation of more abstract concept (e.g., objects) and then infers that of more specific concepts (e.g., parts) by conditioning on the corresponding abstract concept. This makes the model avoid a difficult problem of routing between parts and whole. In experiments on images containing multiple objects with different shapes and part compositions, we demonstrate that our model can learn the latent hierarchical structure between parts and wholes and generate imaginary scenes.
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning
Authors: Desmond Cai, Shiau Hong Lim, Laura Wynter

One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this invariance property occurs: financial portfolio optimization and meta federated learning.