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

Title: Connections Between Optimization in Machine Learning and Adaptive Control

Paper content:
This paper explores many immediate connections between adaptive control and machine learning, both through common update laws as well as common concepts.
Adaptive control as a field has focused on mathematical rigor and guaranteed convergence.
The rapid advances in machine learning on the other hand have brought about a plethora of new techniques and problems for learning.
This paper elucidates many of the numerous common connections between both fields such that results from both may be leveraged together to solve new problems.
In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.
The fields of adaptive control and machine learning have evolved in parallel over the past few decades, with a significant overlap in goals, problem statements, and tools.
Machine learning as a field has focused on computer based systems that improve through experience BID16 BID6 BID24 BID17 BID21 BID35 .
Often times the process of learning is encapsulated in the form of a parameterized model, whose parameters are learned in order to approximate a function.
Optimization methods are commonly employed to reduce the function approximation error using any and all available data.
The field of adaptive control, on the other hand, has focused on the process of controlling engineering systems in order to accomplish regulation and tracking of critical variables of interest (e.g. position and force in robotics, Mach number and altitude in aerospace systems, frequency and voltage in power systems) in the presence of uncertainties in the underlying system models, changes in the environment, and unforeseen variations in the overall infrastructure BID48 BID59 Åström & Wittenmark, 1995; BID33 BID49 .
The approach used for accomplishing such regulation and tracking in adaptive control is the learning of underlying parameters through an online estimation algorithm.
Stability theory is employed for enabling guarantees for the safe evolution of the critical variables, and convergence of the regulation and tracking errors to zero.Learning parameters of a model in both machine learning and adaptive control occurs through the use of input-output data.
In both cases, the main algorithm used for updating the parameters is often based on a gradient descent-like algorithm.
Related tools of analysis, convergence, and robustness in both fields have a tremendous amount of similarity.
As the scope of problems in both fields increases, the associated complexity and challenges increase as well.
Therefore it is highly attractive to understand these similarities and connections so that the two communities can develop new methods for addressing new challenges.

assistant: text: History of parallel developments in update laws and concepts between adaptive control and optimization in machine learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Semi-Supervised Anomaly Detection

Paper content:
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Typically anomaly detection is treated as an unsupervised learning problem.
In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous.
Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples.
Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific.
In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection.
Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution.
We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.

assistant: text: Proposal for an abnormal detection framework under settings where unlabeled data, labeled positive data, and labeled negative data are available, and proposal to approach semi-supervised AD from an information theoretic perspective.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Autonomous Scheduling of Agile Spacecraft Constellations with Delay Tolerant Networking for Reactive Imaging

Paper content:
Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice.
When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage.
In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation.
The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution.
The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest.
In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control.
We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering.
We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods.
The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.
Response and revisit requirements for Earth Observation (EO) vary significantly by application, ranging from less than an hour to monitor disasters, to daily for meteorology, to weekly for land cover monitoring BID33 .
Geostationary satellites provide frequent revisits, but at the cost of coarse spatial resolution, extra launch costs and no polar access.
Lower Earth Orbit satellites overcome these shortcomings, but need numbers and coordination to make up for response characteristics.
Adding agility to satellites and autonomy to the constellation improves the revisit/response for the same number of satellites Copyright © 2019.
All rights reserved.
in given orbits.
However, human operators are expected to scale linearly with constellation nodes BID9 ) and operations staffing may be very costly.

assistant: text: We propose an algorithmic framework to schedule constellations of small spacecraft with 3-DOF re-orientation capabilities, networked with inter-sat links.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: In-training Matrix Factorization for Parameter-frugal Neural Machine Translation

Paper content:
In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation.
Using in-training matrix factorization, parameter matrices may be decomposed into the products of smaller matrices, which can compress large machine translation architectures by vastly reducing the number of learnable parameters.
We apply in-training matrix factorization to different layers of standard neural architectures and show that in-training factorization is capable of reducing nearly 50% of learnable parameters without any associated loss in BLEU score.
Further, we find that in-training matrix factorization is especially powerful on embedding layers, providing a simple and effective method to curtail the number of parameters with minimal impact on model performance, and, at times, an increase in performance.

assistant: text: This paper proposes to compress models using matrix factorization during training for deep neural networks of machine translation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Classical Diffusion: Ballistic Graph Neural Network

Paper content:
This paper presents the ballistic graph neural network.
Ballistic graph neural network tackles the weight distribution from a transportation perspective and has many different properties comparing to the traditional graph neural network pipeline.
The ballistic graph neural network does not require to calculate any eigenvalue.
The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$).
We use a perturbed coin operator to perturb and optimize the diffusion rate.
Our results show that by selecting the diffusion speed, the network can reach a similar accuracy with fewer parameters.
We also show the perturbed filters act as better representations comparing to pure ballistic ones.
We provide a new perspective of training graph neural network, by adjusting the diffusion rate, the neural network's performance can be improved.

assistant: text: A new perspective on how to collect the correlation between nodes based on diffusion properties.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bamboo: Ball-Shape Data Augmentation Against Adversarial Attacks from All Directions

Paper content:
Deep neural networks (DNNs) are widely adopted in real-world cognitive applications because of their high accuracy.
The robustness of DNN models, however, has been recently challenged by adversarial attacks where small disturbance on input samples may result in misclassification.
State-of-the-art defending algorithms, such as adversarial training or robust optimization, improve DNNs' resilience to adversarial attacks by paying high computational costs.
Moreover, these approaches are usually designed to defend one or a few known attacking techniques only.
The effectiveness to defend other types of attacking methods, especially those that have not yet been discovered or explored, cannot be guaranteed.
This work aims for a general approach of enhancing the robustness of DNN models under adversarial attacks.
In particular, we propose Bamboo -- the first data augmentation method designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms.
Bamboo augments the training data set with a small amount of data uniformly sampled on a fixed radius ball around each training data and hence, effectively increase the distance between natural data points and decision boundary.
Our experiments show that Bamboo substantially improve the general robustness against arbitrary types of attacks and noises, achieving better results comparing to previous adversarial training methods, robust optimization methods and other data augmentation methods with the same amount of data points.

assistant: text: Proposes a data augmentation training method to gain model robustness against adversarial perturbations, by augmenting uniformly random samples from a fixed-radius sphere centered at training data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Disentangling neural mechanisms for perceptual grouping

Paper content:
Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.
This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons.
However, the relative contributions of these connections to perceptual grouping are poorly understood.
We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level "Gestalt" vs. high-level object cues for perceptual grouping.
We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing.
Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities, whereas top-down connections rescue learning on tasks with high-level object cues by modifying coarse predictions about the position of the target object.
Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.

assistant: text: Horizontal and top-down feedback connections are responsible for complementary perceptual grouping strategies in biological and recurrent vision systems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks

Paper content:
The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs).
However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters.
This work applies Riemannian stochastic gradient descent (RSGD) to train core tensors of parameters in the Riemannian Manifold before finding vectors of lower Tensor-Train ranks for parameters.
The paper first presents the RSGD algorithm with a convergence analysis and then tests it on more advanced Tensor-Train RNNs such as bi-directional GRU/LSTM and Encoder-Decoder RNNs with a Tensor-Train attention model.
The experiments on digit recognition and machine translation tasks suggest the effectiveness of the RSGD algorithm for Tensor-Train RNNs.

assistant: text: The paper proposes to use Riemannian stochastic gradient algorithm for low-rank tensor train learning in deep networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning from Between-class Examples for Deep Sound Recognition

Paper content:
Deep learning methods have achieved high performance in sound recognition tasks.
Deciding how to feed the training data is important for further performance improvement.
We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).
Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.
We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.
We then input the mixed sound to the model and train the model to output the mixing ratio.
The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.
The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.
Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.
As a result, we achieved a performance surpasses the human level.

assistant: text: Authors defined a new learning task that requires a DNN to predict mixing ratio between sounds from two different classes to increase discriminitive power of the final learned network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data

Paper content:
Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping.
Even though some combinations of transformations might never appear (e.g. an upright face with a horizontal nose), current equivariant architectures consider the set of all possible transformations in a transformation group when learning feature representations.
Contrarily, the human visual system is able to attend to the set of relevant transformations occurring in the environment and utilizes this information to assist and improve object recognition.
Based on this observation, we modify conventional equivariant feature mappings such that they are able to attend to the set of co-occurring transformations in data and generalize this notion to act on groups consisting of multiple symmetries.
We show that our proposed co-attentive equivariant neural networks consistently outperform conventional rotation equivariant and rotation & reflection equivariant neural networks on rotated MNIST and CIFAR-10.

assistant: text: This paper combines attention with group equivariance, specifically looking at the p4m group of rotations, translations, and flips, and derives a form of self-attention that doesn't destroy the equivariance property.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Value Propagation Networks

Paper content:
We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments.
We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems.
We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.

assistant: text: The paper presents an extension of the original value iteration networks (VIN) by considering a state-dependent transition function.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior

Paper content:
We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.
The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.
Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).
Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

assistant: text: The authors suggest that statistical mechanics ideas will help to understand generalization properties of deep neural networks, and give an approach that provides strong qualitative descriptions of empirical results regarding deep neural networks and learning algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY

Paper content:
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.
Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its performance.
Although Adam appears to be a natural solution, its performance in AltQ has rarely been studied before.
In this paper, we first provide a solid exploration on how well AltQ performs with Adam.
We then take a further step to improve the implementation by adopting the technique of parameter restart.
More specifically, the proposed algorithms are tested on a batch of Atari 2600 games and exhibit superior performance than the DQN learning method.
The convergence rate of the slightly modified version of the proposed algorithms is characterized under the linear function approximation.
To the best of our knowledge, this is the first theoretical study on the Adam-type algorithms in Q-learning.

assistant: text: This paper provides a convergence result for traditional Q-learning with linear function approximation when using an Adam-like update.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Paper content:
A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.
In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.
We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.
To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.
We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.
Our experiments show that the proposed methods lead to significant improvements on cooperative problems.
We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.

assistant: text: This paper proposes two methods building upon MADDPG to encourage collaboration amongst decentralized MARL agents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Melody Generation for Pop Music via Word Representation of Musical Properties

Paper content:
Automatic melody generation for pop music has been a long-time aspiration for
both AI researchers and musicians.
However, learning to generate euphonious
melody has turned out to be highly challenging due to a number of factors.
Representation
of multivariate property of notes has been one of the primary challenges.
It is also difficult to remain in the permissible spectrum of musical variety, outside
of which would be perceived as a plain random play without auditory pleasantness.
Observing the conventional structure of pop music poses further challenges.
In this paper, we propose to represent each note and its properties as a unique
‘word,’ thus lessening the prospect of misalignments between the properties, as
well as reducing the complexity of learning.
We also enforce regularization policies
on the range of notes, thus encouraging the generated melody to stay close
to what humans would find easy to follow.
Furthermore, we generate melody
conditioned on song part information, thus replicating the overall structure of a
full song.
Experimental results demonstrate that our model can generate auditorily
pleasant songs that are more indistinguishable from human-written ones than
previous models.

assistant: text: The paper proposes to facilitate generation of melody by representing notes as "words", representing all of the note's properties and thus allowing the generation of musical "sentences".
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Write by Learning the Objective

Paper content:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.
Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.
We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.
In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.
Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.
Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.

assistant: text: This paper proposes to bring together multiple inductive biases that hope to correct for inconsistencies in sequence decoding and proposes to optimize for the parameters of a pre-defined combination of various sub-objectives.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Global Additive Explanations for Neural Nets Using Model Distillation

Paper content:
Interpretability has largely focused on local explanations, i.e. explaining why a model made a particular prediction for a sample.
These explanations are appealing due to their simplicity and local fidelity.
However, they do not provide information about the general behavior of the model.
We propose to leverage model distillation to learn global additive explanations that describe the relationship between input features and model predictions.
These global explanations take the form of feature shapes, which are more expressive than feature attributions.
Through careful experimentation, we show qualitatively and quantitatively that global additive explanations are able to describe model behavior and yield insights about models such as neural nets.
A visualization of our approach applied to a neural net as it is trained is available at https://youtu.be/ErQYwNqzEdc

assistant: text: This paper incorporates Generalized Additive Models (GAMs) with model distillation to provide global explanations of neural nets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Convolutionary, Evolutionary, Revolutionary: What’s next for Bodies, Brains and AI?

Paper content:
In recent years we have made significant progress identifying computational principles that underlie neural function.
While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities.
I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI.
Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.

assistant: text: Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: JAUNE: Justified And Unified Neural language Evaluation

Paper content:
We review the limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, and introduce JAUNE:  a set of criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.

assistant: text: Introduces JAUNE: a methodology to replace BLEU and ROUGE score with multidimensional, model-based evaluators for assessing summaries
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Understanding Knowledge Graph Representation

Paper content:
Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred.
To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping.
Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained.
We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities.
For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.

assistant: text: This paper attempts to understand the latent structure underlying knowledge graph embedding methods, and demonstrates that a model's ability to represent a relation type depends on the model architecture's limitations with respect to relation conditions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Paper content:
Modern deep neural networks (DNNs) require high memory consumption and large computational loads.
In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including the line of works on factorization methods.
Factorization methods approximate the weight matrix of a DNN layer with multiplication of two or multiple low-rank matrices.
However, it is hard to measure the ranks of DNN layers during the training process.
Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step.
The former approach usually induces a high accuracy loss while the latter prevents DNN factorization from efficiently reaching a high compression rate.
In this work, we propose SVD training, which first applies SVD to decompose DNN's layers and then performs training on the full-rank decomposed weights.
To improve the training quality and convergence, we add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding.
Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer.
Singular value pruning is applied at the end to reach a low-rank model.
We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.

assistant: text: Efficiently inducing low-rank deep neural networks via SVD training with sparse singular values and orthogonal singular vectors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PDE-Net: Learning PDEs from Data

Paper content:
Partial differential equations (PDEs)  play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc.
PDEs are commonly derived based on physical laws or empirical observations.
However, the governing equations for many complex systems in modern applications are still not fully known.
With the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored.
Such vast quantity of data offers new opportunities for data-driven discovery of hidden physical laws.
Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models.
The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.
Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses.
A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network.
These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory).
We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet).
Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.

assistant: text: The paper expores the use of deep learning machinery for the purpose of identifying dynamical systems specified by PDEs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Paper content:
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency.
To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision.
Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models.
We also demonstrate ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks -- the highest scores to date.
Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary.
We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise.
The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates.
By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing.
Sensitivity analysis indicates that these techniques, coupled with proper activation function range calibration, offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.

assistant: text: Finetuning after quantization matches or exceeds full-precision state-of-the-art networks at both 8- and 4-bit quantization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visual Imitation with a Minimal Adversary

Paper content:
High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.
In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?
We show that adversarial imitation can work well even in this high dimensional observation space.
Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.
Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.
Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

assistant: text: Imitation from pixels, with sparse or no reward, using off-policy RL and a tiny adversarially-learned reward function.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tandem Blocks in Deep Convolutional Neural Networks

Paper content:
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks.
The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory.
We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers.
We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small (100k--1.2M parameter) image classification networks.
Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts.
Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.

assistant: text: This paper performs an analysis of shortcut connections in ResNet-like architectures, and proposes to substitute the identity shortcuts with an alternative convolutional one referred to as tandem block.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion

Paper content:
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details.
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery.
To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion:
(i) co-registration,
(ii) fusion,
(iii) up-sampling, and
(iv) registration-at-the-loss.
Co-registration of low-res views is learned implicitly through a reference-frame channel, with no explicit registration mechanism.
We learn a global fusion operator that is applied recursively on an arbitrary number of low-res pairs.
We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet.
We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth observation data at scale.
Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.

assistant: text: This paper proposes a framework including recursive fusion to co-registration loss to solve the problem of super-resolution results and high-resolution labels not being pixel aligned.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Cramer Distance as a Solution to Biased Wasserstein Gradients

Paper content:
The Wasserstein probability metric has received much attention from the machine learning community.
Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes.
The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning.
In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients.
The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third.
We provide empirical evidence suggesting this is a serious issue in practice.
Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance.
We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.
We give empirical results on a number of domains comparing these three divergences.
To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.

assistant: text: The Wasserstein distance is hard to minimize with stochastic gradient descent, while the Cramer distance can be optimized easily and works just as well.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning From Noisy Singly-labeled Data

Paper content:
Supervised learning depends on annotated examples, which are taken to be the ground truth.
But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk.
Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem).
Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples.
This raises two fundamental questions: (1) How can we best learn from noisy workers?
(2) How should we allocate our labeling budget to maximize the performance of a classifier?
We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.
The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality.
Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality.
We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold.
Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.

assistant: text: Proposes a supervised learning algorithm for modeling label and worker quality and utilizes algorithm to study how much redundancy is required in crowdsourcing and whether low redundancy with abundant noise examples lead to better labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diversity-Sensitive Conditional Generative Adversarial Networks

Paper content:
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative  Adversarial  Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code.
To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes.
The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives.
Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity.
We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction.
We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

assistant: text: The paper proposes a method for generating diverse outputs for various conditional GAN frameworks including image-to-image translation, image-inpainting, and video prediction, which can be applied to various conditional synthesis frameworks for various tasks.