nils-herrmann/clean_scirepeval huggingface dataset:
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS
Authors: Lin Chen, Sheng Xu

We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere $\mathbb{S}^{d-1}$ and when it is defined on the entire $\mathbb{R}^d$.
How Does Learning Rate Decay Help Modern Neural Networks?
Authors: Kaichao You, Mingsheng Long, Jianmin Wang, Michael I. Jordan

Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent: 1) an initially large learning rate accelerates training or helps the network escape spurious local minima; 2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation. Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex. We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity. And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets. We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.
Learning Stochastic Behaviour from Aggregate Data
Authors: Shaojun Ma, Shu Liu, Hongyuan Zha, Hao-Min Zhou

Learning nonlinear dynamics from aggregate data is a challenging problem since the full trajectory of each individual is not available, namely, the individual observed at one time point may not be observed at next time point, or the identity of individual is unavailable. This is in sharp contrast to learning dynamics with trajectory data, on which the majority of existing methods are based. We propose a novel method using the weak form of Fokker Planck Equation (FPE) to describe density evolution of data in a sampling form, which is then combined with Wasserstein generative adversarial network (WGAN) in training process. In such a sample-based framework we are able to study nonlinear dynamics from aggregate data without solving the partial differential equation (PDE). The model can also handle high dimensional cases with the help of deep neural networks. We demonstrate our approach in the context of a series of synthetic and real-world data sets.
Learning a Behavioral Repertoire from Demonstrations
Authors: Niels Justesen, Miguel González Duque, Daniel Cabarcas Jaramillo, Jean-Baptiste Mouret, Sebastian Risi

Learning (IL) is a machine learning approach to learn a policy from a set of demonstrations. IL can be useful to kick-start learning before applying reinforcement learning (RL) but it can also be useful on its own, e.g. to learn to imitate human players in video games. However, a major limitation of current IL approaches is that they learn only a single ``"average" policy based on a dataset that possibly contains demonstrations of numerous different types of behaviors. In this paper, we present a new approach called Behavioral Repertoire Imitation Learning (BRIL) that instead learns a repertoire of behaviors from a set of demonstrations by augmenting the state-action pairs with behavioral descriptions. The outcome of this approach is a single neural network policy conditioned on a behavior description that can be precisely modulated. We apply this approach to train a policy on 7,777 human demonstrations for the build-order planning task in StarCraft II. Dimensionality reduction techniques are applied to construct a low-dimensional behavioral space from the high-dimensional army unit composition of each demonstration. The results demonstrate that the learned policy can be effectively manipulated to express distinct behaviors. Additionally, by applying the UCB1 algorithm, the policy can adapt its behavior -in-between games- to reach a performance beyond that of the traditional IL baseline approach.
Learning Semantically Meaningful Representations Through Embodiment
Authors: Viviane Clay, Peter König, Kai-Uwe Kühnberger, Gordon Pipa

How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment? Here we investigate embodiment and a closed loop between action and perception as one key component in this process. We take a close look at the representations learned by a deep reinforcement learning agent that is trained with visual and vector observations collected in a 3D environment with sparse rewards. We show that this agent learns semantically meaningful and stable representations of its environment without receiving any semantic labels. Our results show that the agent learns to represent the action relevant information extracted from pixel input in a wide variety of sparse activation patterns. The quality of the representations learned shows the strength of embodied learning and its advantages over fully supervised approaches with regards to robustness and generalizability.
Neural Communication Systems with Bandwidth-limited Channel
Authors: Karen Ullrich, Fabio Viola, Danilo J. Rezende

Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory.  One of the most important aspects of real world communication is that it may happen at varying levels of information transfer. The bandwidth-limited channel models this phenomenon.  In this study we consider learning joint coding with the bandwidth-limited channel.  Although, classical results suggest that it is asymptotically optimal to separate the sub-tasks of compression (source coding) and error correction (channel coding), it is well known that for finite block-length problems, and when there are restrictions to the computational complexity of coding, this optimality may not be achieved. Thus, we empirically compare the performance of joint and separate systems, and conclude that joint systems outperform their separate counterparts when coding is performed by flexible learnable function approximators such as neural networks.  Specifically, we cast the joint communication problem as a variational learning problem. To facilitate this, we introduce a differentiable and computationally efficient version of this channel.  We show that our design compensates for the loss of information by two mechanisms: (i) missing information is modelled by a prior model incorporated in the channel model, and (ii) sampling from the joint model is improved by auxiliary latent variables in the decoder. Experimental results justify the validity of our design decisions through improved distortion and FID scores.
Cut-and-Paste Neural Rendering
Authors: Anand Bhattad, David Forsyth

Cut-and-paste methods take an object from one image and insert it into another.   Doing so often results in unrealistic looking images because the inserted object's shading is inconsistent with the target scene's shading. Existing reshading methods require a geometric and physical model of the inserted object, which is then rendered using environment parameters. Accurately constructing such a model only from a single image is beyond the current understanding of computer vision.

We describe an alternative procedure -- cut-and-paste neural rendering, to render the inserted fragment's shading field consistent with the target scene.  We use a Deep Image Prior (DIP) as a neural renderer trained to render an image with consistent image decomposition inferences.  The resulting rendering from DIP should have an albedo consistent with composite albedo; it should have a shading field that, outside the inserted fragment, is the same as the target scene's shading field; 
and composite surface normals are consistent with the final rendering's shading field. 
The result is a simple procedure that produces convincing and realistic shading. Moreover, our procedure does not require rendered images or image-decomposition from real images in the training or labeled annotations. In fact, our only use of simulated ground truth is our use of a pre-trained normal estimator. Qualitative results are strong, supported by a user study comparing against state-of-the-art image harmonization baseline.
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Authors: Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy

We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.
Adversarially Regularized Autoencoders
Authors: Junbo (Jake) Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun

While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging. In particular, discrete inputs make it more difficult to learn a smooth encoder that preserves the complex local relationships in the input space. In this work, we propose an adversarially regularized autoencoder (ARAE) with the goal of learning more robust discrete-space representations. ARAE jointly trains both a rich discrete-space encoder, such as an RNN, and a simpler continuous space generator function, while using generative adversarial network (GAN) training to constrain the distributions to be similar. This method yields a smoother contracted code space that maps similar inputs to nearby codes, and also an implicit latent variable GAN model for generation. Experiments on text and discretized images demonstrate that the GAN model produces clean interpolations and captures the multimodality of the original space, and that the autoencoder produces improvements in semi-supervised learning as well as state-of-the-art results in unaligned text style transfer task using only a shared continuous-space representation.
Prior Preference Learning From Experts: Designing A Reward with Active Inference
Authors: Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang

Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. An agent will choose an action that leads to its prior preference for a future observation. In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them. We extend the concept of expected free energy (EFE), which is a core quantity in active inference, and claim that EFE can be treated as a negative value function. Motivated by the concept of prior preference and a theoretical connection, we propose a simple but novel method for learning a prior preference from experts. This illustrates that the problem with RL can be approached with a new perspective of active inference. Experimental results of prior preference learning show the possibility of active inference with EFE-based rewards and its application to an inverse RL problem.
Clearing the Path for Truly Semantic Representation Learning
Authors: Dominik Zietlow, Michal Rolinek, Georg Martius

The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand, there are theoretical arguments suggesting impossibility of unsupervised disentanglement. In this work, we show that small perturbations of existing datasets hide the convenient correlation structure that is easily exploited by VAE-based architectures. To demonstrate this, we construct modified versions of the standard datasets on which (i) the generative factors are perfectly preserved; (ii) each image undergoes a transformation barely visible to the human eye; (iii) the leading disentanglement architectures fail to produce disentangled representations. We intend for these datasets to play a role in separating correlation-based models from those that discover the true causal structure.

The construction of the modifications is non-trivial and relies on recent progress on mechanistic understanding of $\beta$-VAEs and their connection to PCA, while also providing additional insights that might be of stand-alone interest.
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Authors: Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. 
Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an operation similar to a finite-difference approximation of the gradient.
That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales. 
Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion. The Deep GA successfully evolves networks with over four million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm. These results (1) expand our sense of the scale at which GAs can operate, (2) suggest intriguingly that in some cases following the gradient is not the best choice for optimizing performance, and (3) make immediately available the multitude of neuroevolution techniques that improve performance. We demonstrate the latter by showing that combining DNNs with novelty search, which encourages exploration on tasks with deceptive or sparse reward functions, can solve a high-dimensional problem on which reward-maximizing algorithms (e.g.\  DQN, A3C, ES, and the GA) fail. Additionally, the Deep GA is faster than ES, A3C, and DQN (it can train Atari in {\raise.17ex\hbox{$\scriptstyle\sim$}}4 hours on one workstation or {\raise.17ex\hbox{$\scriptstyle\sim$}}1 hour distributed on 720 cores), and enables a state-of-the-art, up to 10,000-fold compact encoding technique.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Authors: SHIYANG LI, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Rajani, Xifeng Yan, Yingbo Zhou, Caiming Xiong

Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held- out conversations is less understood. We propose controllable counterfactuals (COCO) to bridge this gap and evaluate dialogue state tracking (DST) models on novel scenarios, i.e., would the system successfully tackle the request if the user responded differently but still consistently with the dialogue flow? COCO leverages turn-level belief states as counterfactual conditionals to produce novel conversation scenarios in two steps: (i) counterfactual goal generation at turn- level by dropping and adding slots followed by replacing slot values, (ii) counterfactual conversation generation that is conditioned on (i) and consistent with the dialogue flow. Evaluating state-of-the-art DST models on MultiWOZ dataset with COCO-generated counterfactuals results in a significant performance drop of up to 30.8% (from 49.4% to 18.6%) in absolute joint goal accuracy. In comparison, widely used techniques like paraphrasing only affect the accuracy by at most 2%. Human evaluations show that COCO-generated conversations perfectly reflect the underlying user goal with more than 95% accuracy and are as human-like as the original conversations, further strengthening its reliability and promise to be adopted as part of the robustness evaluation of DST models.
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?
Authors: Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso

Deep reinforcement learning (DRL) algorithms have demonstrated progress in learning to find a goal in challenging environments. As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments. Yet, from experiments and analysis in this earlier work, it is not clear what strategies are used by these algorithms in navigating the mazes and finding the goal. In this paper, we pose and study this underlying question: are DRL algorithms doing some form of mapping and/or path-planning? Our experiments show that the algorithms are not memorizing the maps of mazes at the testing stage but, rather, at the training stage. Hence, the DRL algorithms fall short of qualifying as mapping or path-planning algorithms with any reasonable definition of mapping. We extend the experiments in Mirowski et al. (2016) by separating the set of training and testing maps and by a more ablative coverage of the space of experiments. Our systematic experiments show that the NavA3C-D1-D2-L algorithm, when trained and tested on the same maps, is able to choose the shorter paths to the goal. However, when tested on unseen maps the algorithm utilizes a wall-following strategy to find the goal without doing any mapping or path planning.
Recurrent Auto-Encoder Model for Multidimensional Time Series Representation
Authors: Timothy Wong, Zhiyuan Luo

Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summarised information can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector can therefore represent features only in the selected dimensions. In addition, we propose using rolling fixed window approach to generate samples. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed through additional visualisation and unsupervised clustering techniques. 

This proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose where clusters of the vector representations can be used to reflect the operating states of selected aspects of the industrial system.
Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding
Authors: Nir Baram, Tom Zahavy, Shie Mannor

Recent progress in the field of Reinforcement Learning (RL) has enabled to tackle bigger and more challenging tasks. However, the increasing complexity of the problems, as well as the use of more sophisticated models such as Deep Neural Networks (DNN), impedes the understanding of artificial agents behavior. In this work, we present the Semi-Aggregated Markov Decision Process (SAMDP) model. The purpose of the SAMDP modeling is to describe and allow a better understanding of complex behaviors by identifying temporal and spatial abstractions. In contrast to other modeling approaches, SAMDP is built in a transformed state-space that encodes the dynamics of the problem. We show that working with the \emph{right} state representation mitigates the problem of finding spatial and temporal abstractions. We describe the process of building the SAMDP model from observed trajectories and give examples for using it in a toy problem and complicated DQN policies. Finally, we show how using the SAMDP we can monitor the policy at hand and make it more robust.
From SCAN to Real Data: Systematic Generalization via Meaningful Learning
Authors: Ning Shi, Boxin Wang, Wei Wang, Xiangyu Liu, Rong Zhang, Hui Xue', Xinbing Wang, Zhouhan Lin

Humans can systematically generalize to novel compositions of existing concepts. There have been extensive conjectures into the extent to which neural networks can do the same. Recent arguments supported by evidence on the SCAN dataset claim that neural networks are inherently ineffective in such cognitive capacity. In this paper, we revisit systematic generalization from the perspective of meaningful learning, an exceptional capability of humans to learn new concepts by connecting them with other previously known knowledge. We propose to augment a training dataset in either an inductive or deductive manner to build semantic links between new and old concepts. Our observations on SCAN suggest that, following the meaningful learning principle, modern sequence-to-sequence models, including RNNs, CNNs, and Transformers, can successfully generalize to compositions of new concepts. We further validate our findings on two real-world datasets on semantic parsing and consistent compositional generalization is also observed. Moreover, our experiments demonstrate that both prior knowledge and semantic linking play a key role to achieve systematic generalization. Meanwhile, inductive learning generally works better than deductive learning in our experiments. Finally, we provide an explanation for data augmentation techniques by concluding them into either inductive-based or deductive-based meaningful learning. We hope our findings will encourage excavating existing neural networks' potential in systematic generalization through more advanced learning schemes.
Indiscriminate Poisoning Attacks Are Shortcuts
Authors: Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

Indiscriminate data poisoning attacks, which add imperceptible perturbations to training data to maximize the test error of trained models, have become a trendy topic because they are thought to be capable of preventing unauthorized use of data.  In this work, we investigate why these perturbations work in principle. We find that the perturbations of advanced poisoning attacks are almost linear separable when assigned with the target labels of the corresponding samples. This is an important population property for various perturbations that were not unveiled before. Moreover, we further confirm that linear separability is indeed the workhorse for poisoning attacks. We synthesize linear separable data as perturbations and show that such synthetic perturbations are as powerful as the deliberately crafted attacks.  Our finding also suggests that the shortcut learning problem is more serious than previously believed as deep learning heavily relies on shortcuts even if they are of an imperceptible scale and mixed together with the normal features. It also suggests that pre-trained feature extractors can be a powerful defense.