ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Functional Extension of Multi-Output Learning

Paper content:
When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers.
We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.
Several fundamental problems in machine learning and statistics can be phrased as the minimization of a loss function described by a hyperparameter.
The hyperparameter might capture numerous aspects of the problem:
(i) the tolerance w.
r.
t.
outliers as the -insensitivity in Support Vector Regression (Vapnik et al., 1997) ,
(ii) importance of smoothness or sparsity such as the weight of the l 2 -norm in Tikhonov regularization (Tikhonov & Arsenin, 1977) , l 1 -norm in LASSO (Tibshirani, 1996) , or more general structured-sparsity inducing norms BID3 ,
(iii) Density Level-Set Estimation (DLSE), see for example one-class support vector machines One-Class Support Vector Machine (OCSVM, Schölkopf et al., 2000) ,
(iv) confidence as exemplified by Quantile Regression (QR, Koenker & Bassett Jr, 1978) , or
(v) importance of different decisions as implemented by Cost-Sensitive Classification (CSC, Zadrozny & Elkan, 2001) .
In various cases including QR, CSC or DLSE, one is interested in solving the parameterized task for several hyperparameter values.
Multi-Task Learning (Evgeniou & Pontil, 2004 ) provides a principled way of benefiting from the relationship between similar tasks while preserving local properties of the algorithms: ν-property in DLSE (Glazer et al., 2013) or quantile property in QR (Takeuchi et al., 2006) .A
natural extension from the traditional multi-task setting is to provide a prediction tool being able to deal with any value of the hyperparameter. In
their seminal work, (Takeuchi et al., 2013) extended multi-task learning by considering an infinite number of parametrized tasks in a framework called Parametric Task Learning (PTL) . Assuming
that the loss is piecewise affine in the hyperparameter, the authors are able to get the whole solution path through parametric programming, relying on techniques developed by Hastie et al. (2004) .In this paper
1 , we relax the affine model assumption on the tasks as well as the piecewise-linear assumption on the loss, and take a different angle. We propose Infinite
Task Learning (ITL) within the framework of functionvalued function learning to handle a continuum number of parameterized tasks using Vector-Valued Reproducing Kernel Hilbert Space (vv-RKHS, Pedrick, 1957) .

assistant: text: We propose an extension of multi-output learning to a continuum of tasks using operator-valued kernels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

Paper content:
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network.
Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running.
In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion.
The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training.
Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.

assistant: text: Synthesize complex and extended human motions using an auto-conditioned LSTM network
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Learning is Robust to Massive Label Noise

Paper content:
Deep neural networks trained on large supervised datasets have led to impressive results in recent years.
However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained.
In this paper, we investigate the behavior of deep neural networks on training sets with massively noisy labels.
We show on multiple datasets such as MINST, CIFAR-10 and ImageNet that successful learning is possible even with an essentially arbitrary amount of noise.
For example, on MNIST we find that accuracy of above 90 percent is still attainable even when the dataset has been diluted with 100 noisy examples for each clean example.
Such behavior holds across multiple patterns of label noise, even when noisy labels are biased towards confusing classes.
Further, we show how the required dataset size for successful training increases with higher label noise.
Finally, we present simple actionable techniques for improving learning in the regime of high label noise.

assistant: text: We show that deep neural networks are able to learn from data that has been diluted by an arbitrary amount of noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Understanding the Transferability of Deep Representations

Paper content:
Deep neural networks trained on a wide range of datasets demonstrate impressive transferability.
Deep features appear general in that they are applicable to many datasets and tasks.
Such property is in prevalent use in real-world applications.
A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset.
Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations.
This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability.
We demonstrate that
1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset;
2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially.
The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation.
3) The feasibility of transferability is related to the similarity of both input and label.
And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines.
We further provide a theoretical analysis to verify our observations.

assistant: text: Understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction

Paper content:
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.
One way of constructing such representations is by focusing on the important events in a sequence.
In this paper, we propose a model that learns both to discover such key events (or keyframes) as well as to represent the sequence in terms of them.
We do so using a hierarchical Keyframe-Inpainter (KeyIn) model that first generates keyframes and their temporal placement and then inpaints the sequences between keyframes.
We propose a fully differentiable formulation for efficiently learning the keyframe placement.
We show that KeyIn finds informative keyframes in several datasets with diverse dynamics.
When evaluated on a planning task, KeyIn outperforms other recent proposals for learning hierarchical representations.

assistant: text: We propose a model that learns to discover informative frames in a future video sequence and represent the video via its keyframes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning-Based Low-Rank Approximations

Paper content:
We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $||A- A'||_F$.
The algorithm uses a training set of input matrices in order to optimize its performance.
Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection $SA$, where $S$ is a sparse random $m \times n$ “sketching matrix”, and then performing the singular value decomposition of $SA$.
We show how to replace the random matrix $S$ with a “learned” matrix of the same sparsity to reduce the error.
Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude.
We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees.

assistant: text: Learning-based algorithms can improve upon the performance of classical algorithms for the low-rank approximation problem while retaining the worst-case guarantee.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Boundary Seeking GANs

Paper content:
Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions.
GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t.
the generative parameters, and thus do not work for discrete data.
We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs).
We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation.
In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.

assistant: text: We address training GANs with discrete data by formulating a policy gradient that generalizes across f-divergences
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Learning of Additive Second-Order Penalties with  Applications to Fairness

Paper content:
Many notions of fairness may be expressed as linear constraints, and the resulting constrained objective is often optimized by transforming the problem into its Lagrangian dual with additive linear penalties.
In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium.
In this paper, we propose to modify the linear penalties to second-order ones, and we argue that this results in a more practical training procedure in non-convex, large-data settings.
For one, the use of second-order penalties allows training the penalized objective with a fixed value of the penalty coefficient, thus avoiding the instability and potential lack of convergence associated with two-player min-max games.
Secondly, we derive a method for efficiently computing the gradients associated with the second-order penalties in stochastic mini-batch settings.
Our resulting algorithm performs well empirically, learning an appropriately fair classifier on a number of standard benchmarks.

assistant: text: We propose a method to stochastically optimize second-order penalties and show how this may apply to training fairness-aware classifiers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NAMSG: An Efficient Method for Training Neural Networks

Paper content:
We introduce NAMSG, an adaptive first-order algorithm for training neural networks.
The method is efficient in computation and memory, and is straightforward to implement.
It computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting.
It also scales the updating vector elementwise by a nonincreasing preconditioner to take the advantages of AMSGRAD.
We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, and provide a strategy to select the observation factor without grid search.
A data-dependent regret bound is proposed to guarantee the convergence in the convex setting.
The method can further achieve a O(log(T)) regret bound for strongly convex functions.
Experiments demonstrate that NAMSG works well in practical problems and compares favorably to popular adaptive methods, such as ADAM, NADAM, and AMSGRAD.

assistant: text: A new algorithm for training neural networks that compares favorably to popular adaptive methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scaling Laws for the Principled Design, Initialization, and Preconditioning of ReLU Networks

Paper content:
Abstract In this work, we describe a set of rules for the design and initialization of well-conditioned neural networks, guided by the goal of naturally balancing the diagonal blocks of the Hessian at the start of training.
We show how our measure of conditioning of a block relates to another natural measure of conditioning, the ratio of weight gradients to the weights.
We prove that for a ReLU-based deep multilayer perceptron, a simple initialization scheme using the geometric mean of the fan-in and fan-out satisfies our scaling rule.
For more sophisticated architectures, we show how our scaling principle can be used to guide design choices to produce well-conditioned neural networks, reducing guess-work.

assistant: text: A theory for initialization and scaling of ReLU neural network layers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EEG based Emotion Recognition of Image Stimuli 

Paper content:
Emotion is playing a great role in our daily lives.
The necessity and importance of an automatic Emotion recognition system is getting increased.
Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc.
However, these features can potentially be changed to fake features.
So to detect hidden and real features that is not controlled by the person are data measured from brain signals.
There are various ways of measuring brain waves: EEG, MEG, FMRI, etc.
On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work.
The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli.
The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements).
The signals were filtered and type of frequency band was computed and detected.
The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli.
Finally, the performance of the proposed approach was tested.
The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively.
In this study, we also applied EEG applications in the context of neuro-marketing.
The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service.

assistant: text: This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients

Paper content:
Two main families of reinforcement learning algorithms, Q-learning and policy gradients, have recently been proven to be equivalent when using a softmax relaxation on one part, and an entropic regularization on the other.
We relate this result to the well-known convex duality of Shannon entropy and the softmax function.
Such a result is also known as the Donsker-Varadhan formula.
This provides a short proof of the equivalence.
We then interpret this duality further, and use ideas of convex analysis to prove a new policy inequality relative to soft Q-learning.

assistant: text: A short proof of the equivalence of soft Q-learning and policy gradients.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalized Transformation-based Gradient

Paper content:
The reparameterization trick has become one of the most useful tools in the field of variational inference.
However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions.
In this paper, we generalized the reparameterization trick by allowing a general transformation.
We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization.
Based on the proposed gradient model, we propose a new polynomial-based gradient estimator which has better theoretical performance than the reparameterization trick under certain condition and can be applied to a larger class of variational distributions.
In studies of synthetic and real data, we show that our proposed gradient estimator has a significantly lower gradient variance than other state-of-the-art methods thus enabling a faster inference procedure.

assistant: text: a generalized transformation-based gradient model for variational inference
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

Paper content:
We derive a new intrinsic social motivation for multi-agent reinforcement learning (MARL), in which agents are rewarded for having causal influence over another agent's actions, where causal influence is assessed using counterfactual reasoning.
The reward does not depend on observing another agent's reward function, and is thus a more realistic approach to MARL than taken in previous work.
We show that the causal influence reward is related to maximizing the mutual information between agents' actions.
We test the approach in challenging social dilemma environments, where it consistently leads to enhanced cooperation between agents and higher collective reward.
Moreover, we find that rewarding influence can lead agents to develop emergent communication protocols.
Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.
Finally, we show that influence can be computed by equipping each agent with an internal model that predicts the actions of other agents.
This allows the social influence reward to be computed without the use of a centralised controller, and as such represents a significantly more general and scalable inductive bias for MARL with independent agents.

assistant: text: We reward agents for having a causal influence on the actions of other agents, and show that this gives rise to better cooperation and more meaningful emergent communication protocols.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LiTSE: Temporal Knowledge Graph Embedding Model based on Linear Time Series Decomposition

Paper content:
Knowledge Graph Embedding (KGE) has attracted more attention in recent years.
Most of KGE models learn from time-unaware triples.
However, the inclusion of temporal information beside triples would further improve the
performance of a KGE model.
In this regard, we propose LiTSE, a temporal KGE model which incorporates time information
into entity/relation representations by using linear time series decomposition.
Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal
KGs into the space of multi-dimensional Gaussian distributions.
The mean of each entity/ relation embedding at a time step shows the current expected position, whereas its covariance (which is stationary over time) represents
its temporal uncertainty.
Experiments show that LiTSE not only achieves the state-of- the-art on link prediction in temporal KGs, but also has the ability to predict the occurrence time of facts with missing time annotations, as well as the existence of future events.
To the best of our knowledge, no other model is capable to perform all these tasks.

assistant: text: Submitted in EMNLP
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Temporal Difference Variational Auto-Encoder

Paper content:
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics:
(a) it should build an abstract state representing the condition of the world;
(b) it should form a belief which represents uncertainty on the world;
(c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.
Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions.
TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.

assistant: text: Generative model of temporal data, that builds online belief state, operates in latent space, does jumpy predictions and rollouts of states.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NLProlog: Reasoning with Weak Unification for Natural Language Question Answering

Paper content:
Symbolic logic allows practitioners to build systems that perform rule-based reasoning which is interpretable and which can easily be augmented with prior knowledge.
However, such systems are traditionally difficult to apply to problems involving natural language due to the large linguistic variability of language.
Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.
We propose to reap the benefits of both approaches by applying a combination of neural networks and logic programming to natural language question answering.
We propose to employ an external, non-differentiable Prolog prover which utilizes a similarity function over pretrained sentence encoders.
We fine-tune these representations via Evolution Strategies with the goal of multi-hop reasoning on natural language.
This allows us to create a system that can apply rule-based reasoning to natural language and induce domain-specific natural language rules from training data.
We evaluate the proposed system on two different question answering tasks, showing that it complements two very strong baselines – BIDAF (Seo et al., 2016a) and FASTQA (Weissenborn et al.,2017) – and outperforms both when used in an ensemble.

assistant: text: We introduce NLProlog, a system that performs rule-based reasoning on natural language by leveraging pretrained sentence embeddings and fine-tuning with Evolution Strategies, and apply it to two multi-hop Question Answering tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Causal Generative Neural Networks

Paper content:
We introduce CGNN, a framework to learn functional causal models as generative neural networks.
These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data.
Unlike previous approaches, CGNN leverages both conditional independences and distributional asymmetries to seamlessly discover bivariate and multivariate 
 causal structures, with or without hidden variables.
CGNN does not only estimate the causal structure, but a full and differentiable generative model of the data.
Throughout an extensive variety of experiments, we illustrate the competitive  esults of CGNN w.r.t state-of-the-art alternatives in observational causal discovery on both simulated and real data, in the tasks of cause-effect inference, v-structure identification, and multivariate causal discovery.

assistant: text: Discover the structure of functional causal models with generative neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Differentiable Programming for Physical Simulation

Paper content:
We study the problem of learning and optimizing through physical simulations via differentiable programming.
We present DiffSim, a new differentiable programming language tailored for building high-performance differentiable physical simulations.
We demonstrate the performance and productivity of our language in gradient-based learning and optimization tasks on 10 different physical simulators.
For example, a differentiable elastic object simulator written in our language is 4.6x faster than the hand-engineered CUDA version yet runs as fast, and is 188x faster than TensorFlow.
Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations.
Finally, we share the lessons learned from our experience developing these simulators, that is, differentiating physical simulators does not always yield useful gradients of the physical system being simulated.
We systematically study the underlying reasons and propose solutions to improve gradient quality.

assistant: text: We study the problem of learning and optimizing through physical simulations via differentiable programming, using our proposed DiffSim programming language and compiler.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Immersive Visualization of the Classical Non-Euclidean Spaces using Real-Time Ray Tracing

Paper content:
This paper presents a system for immersive visualization of Non-Euclidean spaces using real-time ray tracing.
It exploits the capabilities of the new generation of GPU’s based on the NVIDIA’s Turing architecture in order to develop new methods for intuitive exploration of landscapes featuring non-trivial geometry and topology in virtual reality.

assistant: text: Immersive Visualization of the Classical Non-Euclidean Spaces using Real-Time Ray Tracing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling

Paper content:
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a  surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).
This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives.
The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models.
However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work.

assistant: text: We compare many tasks and task combinations for pretraining sentence-level BiLSTMs for NLP tasks. Language modeling is the best single pretraining task, but simple baselines also do well.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Count-Based Exploration with the Successor Representation

Paper content:
The problem of exploration in reinforcement learning is well-understood in the tabular case and many sample-efficient algorithms are known.
Nevertheless, it is often unclear how the algorithms in the tabular setting can be extended to tasks with large state-spaces where generalization is required.
Recent promising developments generally depend on problem-specific density models or handcrafted features.
In this paper we introduce a simple approach for exploration that allows us to develop theoretically justified algorithms in the tabular case but that also give us intuitions for new algorithms applicable to settings where function approximation is required.
Our approach and its underlying theory is based on the substochastic successor representation, a concept we develop here.
While the traditional successor representation is a representation that defines state generalization by the similarity of successor states, the substochastic successor representation is also able to implicitly count the number of times each state (or feature) has been observed.
This extension connects two until now disjoint areas of research.
We show in traditional tabular domains (RiverSwim and SixArms) that our algorithm empirically performs as well as other sample-efficient algorithms.
We then describe a deep reinforcement learning algorithm inspired by these ideas and show that it matches the performance of recent pseudo-count-based methods in hard exploration Atari 2600 games.

assistant: text: We propose the idea of using the norm of the successor representation an exploration bonus in reinforcement learning. In hard exploration Atari games, our the deep RL algorithm matches the performance of recent pseudo-count-based methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning Neural Bloom Filters

Paper content:
There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression.
In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence.
In many applications this expensive initialization is not practical, for example streaming algorithms --- where inputs are ephemeral and can only be inspected a small number of times.
In this paper we explore the learning of approximate set membership over a stream of data in one-shot via meta-learning.
We propose a novel memory architecture, the Neural Bloom Filter, which we show to be more compressive than Bloom Filters and several existing memory-augmented neural networks in scenarios of skewed data or structured sets.

assistant: text: We investigate the space efficiency of memory-augmented neural nets when learning set membership.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SpectralNet: Spectral Clustering using Deep Neural Networks

Paper content:
Spectral clustering is a leading and popular technique in unsupervised data analysis.
Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension).
In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcomings.
Our network, which we call SpectralNet, learns a map that embeds input data points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them.
We train SpectralNet using a procedure that involves constrained stochastic optimization.
Stochastic optimization allows it to scale to large datasets, while the constraints, which are implemented using a special purpose output layer, allow us to keep the network output orthogonal.
Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points.
To further improve the quality of the clustering, we replace the standard pairwise Gaussian affinities with affinities leaned from unlabeled data using a Siamese network.
Additional improvement can be achieved by applying the network to code representations produced, e.g., by standard autoencoders.
Our end-to-end learning procedure is fully unsupervised.
In addition, we apply VC dimension theory to derive a lower bound on the size of  SpectralNet.
State-of-the-art clustering results are reported for both the MNIST and Reuters datasets.

assistant: text: Unsupervised spectral clustering using deep neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sample Complexity Lower Bounds for Compressive Sensing with Generative Models

Paper content:
The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis.
Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near the range of a suitably-chosen generative model.
In particular, in (Bora {\em et al.}, 2017) it was shown that roughly $O(k\log L)$ random Gaussian measurements suffice for accurate recovery when the $k$-input generative model is bounded and $L$-Lipschitz, and that $O(kd \log w)$ measurements suffice for $k$-input ReLU networks with depth $d$ and width $w$.  In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis.
In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an $L$-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is $\Omega(k \log L)$;
(ii) Using similar ideas, we construct two-layer ReLU networks of high width requiring $\Omega(k \log w)$ measurements, as well as lower-width deep ReLU networks requiring $\Omega(k d)$ measurements.
As a result, we establish that the scaling laws derived in (Bora {\em et al.}, 2017) are optimal or near-optimal in the absence of further assumptions.

assistant: text: We establish that the scaling laws derived in (Bora et al., 2017) are optimal or near-optimal in the absence of further assumptions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model Imitation for Model-Based Reinforcement Learning

Paper content:
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments.
However, due to estimation error, rollouts in the learned model, especially those of long horizon, fail to match the ones in real-world environments.
This mismatching has seriously impacted the sample complexity of MBRL.
The phenomenon can be attributed to the fact that previous works employ supervised learning to learn the one-step transition models, which has inherent difficulty ensuring the matching of distributions from multi-step rollouts.
Based on the claim, we propose to learn the synthesized model by matching the distributions of multi-step rollouts sampled from the synthesized model and the real ones via WGAN.
We theoretically show that matching the two can minimize the difference of cumulative rewards between the real transition and the learned one.
Our experiments also show that the proposed model imitation method outperforms the state-of-the-art in terms of sample complexity and average return.

assistant: text: Our method incorporates WGAN to achieve occupancy measure matching for transition learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Convolutional Sequence Modeling Revisited

Paper content:
This paper revisits the problem of sequence modeling using convolutional
architectures.
Although both convolutional and recurrent architectures have
a
long history in sequence prediction, the current "default" mindset in much of
the deep learning community is that generic sequence modeling is best handled
using recurrent networks.
The goal of this paper is to question this assumption
. 
Specifically, we consider a simple generic temporal convolution network (TCN
),
which adopts features from modern ConvNet architectures such as a dilations and
residual connections.
We show that on a variety of sequence modeling tasks
,
including many frequently used as benchmarks for evaluating recurrent networks
,
the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) and
sometimes even highly specialized approaches.
We further show that the
potential "infinite memory" advantage that RNNs have over TCNs is largely
absent in practice: TCNs indeed exhibit longer effective history sizes than their
recurrent counterparts.
As a whole, we argue that it may be time to (re)consider
ConvNets as the default "go to" architecture for sequence modeling.

assistant: text: We argue that convolutional networks should be considered the default starting point for sequence modeling tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Neural Physics Predictor

Paper content:
Recently,  neural-network  based  forward  dynamics  models  have  been  proposed that  attempt  to  learn  the  dynamics  of  physical  systems  in  a  deterministic  way.
While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to plausible but different trajectories that diverge from the ground truth.
A system that predicts distributions of the future physical states for long time horizons based on its uncertainty is thus a promising solution.
In this work, we introduce a novel robust Monte Carlo sampling based graph-convolutional dropout method that allows us to sample multiple plausible trajectories for an initial state given a neural-network based forward dynamics predictor.
By introducing a new shape preservation loss and training our dynamics model recurrently, we stabilize long-term predictions.
We show that our model’s long-term forward dynamics prediction errors on complicated physical interactions of rigid and deformable objects of various shapes are significantly lower than existing strong baselines.
Lastly, we demonstrate how generating multiple trajectories with our Monte Carlo dropout method can be used to train model-free reinforcement learning agents faster and to better solutions on simple manipulation tasks.

assistant: text: We propose a stochastic differentiable forward dynamics predictor that is able to sample multiple physically plausible trajectories under the same initial input state and show that it can be used to train model-free policies more efficiently.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Extreme Value k-means Clustering

Paper content:
Clustering is the central task in unsupervised learning and data mining.
k-means is one of the most widely used clustering algorithms.
Unfortunately, it is generally non-trivial to extend k-means to cluster data points beyond Gaussian distribution, particularly, the clusters with non-convex shapes (Beliakov & King, 2006).
To this end, we, for the first time, introduce Extreme Value Theory (EVT) to improve the clustering ability of k-means.
Particularly, the Euclidean space was transformed into a novel probability space denoted as extreme value space by EVT.
We thus propose a novel algorithm called Extreme Value k-means (EV k-means), including GEV k-means and GPD k-means.
In addition, we also introduce the tricks to accelerate Euclidean distance computation in improving the computational efficiency of classical k-means.
Furthermore, our EV k-means is extended to an online version, i.e., online Extreme Value k-means, in utilizing the Mini Batch k-means to cluster streaming data.
Extensive experiments are conducted to validate our EV k-means and online EV k-means on synthetic datasets and real datasets.
Experimental results show that our algorithms significantly outperform competitors in most cases.

assistant: text: This paper introduces Extreme Value Theory into k-means to measure similarity and proposes a novel algorithm called Extreme Value k-means for clustering.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Wikipedia by Summarizing Long Sequences

Paper content:
We show that generating English Wikipedia articles can be approached as a multi-
document summarization of source documents.
We use extractive summarization
to coarsely identify salient information and a neural abstractive model to generate
the article.
For the abstractive model, we introduce a decoder-only architecture
that can scalably attend to very long sequences, much longer than typical encoder-
decoder architectures used in sequence transduction.
We show that this model can
generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia
articles.
When given reference documents, we show it can extract relevant factual
information as reflected in perplexity, ROUGE scores and human evaluations.

assistant: text: We generate Wikipedia articles abstractively conditioned on source document text.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Gaussian Policies from Smoothed Action Value Functions

Paper content:
State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning.
We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q-value used in SARSA.
We show that such smoothed Q-values still satisfy a Bellman equation, making them naturally learnable from experience sampled from an environment.
Moreover, the gradients of expected reward with respect to the mean and covariance of a parameterized Gaussian policy can be recovered from the gradient and Hessian of the smoothed Q-value function.
Based on these relationships we develop new algorithms for training a Gaussian policy directly from a learned Q-value approximator.
The approach is also amenable to proximal optimization techniques by augmenting the objective with a penalty on KL-divergence from a previous policy.
We find that the ability to learn both a mean and covariance during training allows this approach to achieve strong results on standard continuous control benchmarks.

assistant: text: We propose a new Q-value function that enables better learning of Gaussian policies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature Partitioning for Efficient Multi-Task Architectures

Paper content:
Multi-task learning promises to use less data, parameters, and time than training separate single-task models.
But realizing these benefits in practice is challenging.
In particular, it is difficult to define a suitable architecture that has enough capacity to support many tasks while not requiring excessive compute for each individual task.
There are difficult trade-offs when deciding how to allocate parameters and layers across a large set of tasks.
To address this, we propose a method for automatically searching over multi-task architectures that accounts for resource constraints.
We define a parameterization of feature sharing strategies for effective coverage and sampling of architectures.
We also present a method for quick evaluation of such architectures with feature distillation.
Together these contributions allow us to quickly optimize for parameter-efficient multi-task models.
We benchmark on Visual Decathlon, demonstrating that we can automatically search for and identify architectures that effectively make trade-offs between task resource requirements while maintaining a high level of final performance.

assistant: text: automatic search for multi-task architectures that reduce per-task feature use
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Robust Rewards with Adverserial Inverse Reinforcement Learning

Paper content:
Reinforcement learning provides a powerful and general framework for decision
making and control, but its application in practice is often hindered by the need
for extensive feature and reward engineering.
Deep reinforcement learning methods
can remove the need for explicit engineering of policy or value features, but
still require a manually specified reward function.
Inverse reinforcement learning
holds the promise of automatic reward acquisition, but has proven exceptionally
difficult to apply to large, high-dimensional problems with unknown dynamics.
In
this work, we propose AIRL, a practical and scalable inverse reinforcement learning
algorithm based on an adversarial reward learning formulation that is competitive
with direct imitation learning algorithms.
Additionally, we show that AIRL is
able to recover portable reward functions that are robust to changes in dynamics,
enabling us to learn policies even under significant variation in the environment
seen during training.

assistant: text: We propose an adversarial inverse reinforcement learning algorithm capable of learning reward functions which can transfer to new, unseen environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Training of Graph Convolutional Networks

Paper content:
Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data.
However, GCN computes nodes' representation recursively from their neighbors, making the receptive field size grow exponentially with the number of layers.
Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds.
In this paper, we develop a preprocessing strategy and two control variate based algorithms to further reduce the receptive field size.
Our algorithms are guaranteed to converge to GCN's local optimum regardless of the neighbor sampling size.
Empirical results show that our algorithms have a similar convergence speed per epoch with the exact algorithm even using only two neighbors per node.
The time consumption of our algorithm on the Reddit dataset is only one fifth of previous neighbor sampling algorithms.

assistant: text: A control variate based stochastic training algorithm for graph convolutional networks that the receptive field can be only two neighbors per node.