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

Title: Buy 4 REINFORCE Samples, Get a Baseline for Free!

Paper content:
REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective.
However, the common case of sampling one prediction per datapoint (input) is data-inefficient.
We show that by drawing multiple samples (predictions) per datapoint, we can learn with significantly less data, as we freely obtain a REINFORCE baseline to reduce variance.
Additionally we derive a REINFORCE estimator with baseline, based on sampling without replacement.
Combined with a recent technique to sample sequences without replacement using Stochastic Beam Search, this improves the training procedure for a sequence model that predicts the solution to the Travelling Salesman Problem.

assistant: text: We show that by drawing multiple samples (predictions) per input (datapoint), we can learn with less data as we freely obtain a REINFORCE baseline.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Noise Regularization for Conditional Density Estimation

Paper content:
Modelling statistical relationships beyond the conditional mean is crucial in many settings.
Conditional density estimation (CDE) aims to learn the full conditional probability density from data.
Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective.
Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective.
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency.
In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models.
The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce.

assistant: text: A model-agnostic regularization scheme for neural network-based conditional density estimation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantum Optical Experiments Modeled by Long Short-Term Memory

Paper content:
We demonstrate how machine learning is able to model experiments in quantum physics.
Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography.
Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels.
Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it.
To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states.
In this work, we show that machine learning models can provide significant improvement over random search.
We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves.
This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

assistant: text: We demonstrate how machine learning is able to model experiments in quantum physics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Complex- and Real-Valued Neural Network Architectures

Paper content:
Complex-value neural networks are not a new concept, however, the use of real-values has often been favoured over complex-values due to difficulties in training and accuracy of results.
Existing literature ignores the number of parameters used.
We compared complex- and real-valued neural networks using five activation functions.
We found that when real and complex neural networks are compared using simple classification tasks, complex neural networks perform equal to or slightly worse than real-value neural networks.
However, when specialised architecture is used, complex-valued neural networks outperform real-valued neural networks.
Therefore, complex–valued neural networks should be used when the input data is also complex or it can be meaningfully to the complex plane,  or when the network architecture uses the structure defined by using complex numbers.

assistant: text: Comparison of complex- and real-valued multi-layer perceptron with respect to the number of real-valued parameters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design

Paper content:
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.
Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency.
To that end, we leverage the posterior regularization framework andshow that this constraint satisfaction problem can be formulated as sampling froma Gibbs distribution.
The main challenges come from the black-box nature ofthose physical constraints, since they are obtained via solving highly non-linearPDEs.
To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling.
We explore two surrogate approaches.
The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin.
In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers.
The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model.
We prove the convergence of those two approaches when the target distribution is log-concave and smooth.
We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.

assistant: text: We propose surrogate based Constrained Langevin sampling with application in nano-porous material configuration design.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs

Paper content:
Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the “meaning” of text and a form of unsupervised learning useful for downstream tasks.
However, their power is not theoretically understood.
The current paper derives formal understanding by looking at the subcase of linear embedding schemes.
Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text.
This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show.
Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods.
We also show a surprising new property of embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, the standard sparse recovery tool, which may explain why they lead to better representations in practice.

assistant: text: We use the theory of compressed sensing to prove that LSTMs can do at least as well on linear text classification as Bag-of-n-Grams.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: What is image captioning made of?

Paper content:
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space.
To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant.
We propose a sparse bag-of-objects vector as an interpretable representation to investigate our distributional similarity hypothesis.
We found that image captioning models
(i) are capable of separating structure from noisy input representations;
(ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space;
(iii) cluster images with similar visual and linguistic information together;
(iv) are heavily reliant on test sets with a similar distribution as the training set;
(v) repeatedly generate the same captions by matching images and ‘retrieving’ a caption in the joint visual-textual space.
Our experiments all point to one fact: that our distributional similarity hypothesis holds.
We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.

assistant: text: This paper presents an empirical analysis on the role of different types of image representations and probes the properties of these representations for the task of image captioning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reducing Distant Supervision Noise with Maxpooled Attention and Sentence-Level Supervision

Paper content:
We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision.
We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities.
We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling.
The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FB-NYT dataset.

assistant: text: A new form of attention that works well for the distant supervision setting, and a multitask learning approach to add sentence-level annotations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CLN2INV: Learning Loop Invariants with Continuous Logic Networks

Paper content:
Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs.
Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops.
In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces.
Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT)  for loop invariants from program execution traces.
We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently.
We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset.
CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset.
Moreover, CLN2INV takes only 1.1 second on average for each problem, which is 40 times faster than existing approaches.
We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset.

assistant: text: We introduce the Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants and general SMT formulas.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Smooth markets: A basic mechanism for organizing gradient-based learners

Paper content:
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.
Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games.
We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions.
SM-games codify a common design pattern in machine learning that includes some GANs, adversarial training, and other recent algorithms.
We show that SM-games are amenable to analysis and optimization using first-order methods.

assistant: text: We introduce a class of n-player games suited to gradient-based methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diffusion Scattering Transforms on Graphs

Paper content:
Stability is a key aspect of data analysis.
In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision.
Scattering transforms construct deep convolutional representations which are certified stable to input deformations.
This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain.
In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps.
The resulting representation is stable to metric perturbations of the domain while being able to capture ''high-frequency'' information, akin to the Euclidean Scattering.

assistant: text: Stability of scattering transform representations of graph data to deformations of the underlying graph support.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DP-LSSGD: An Optimization Method to Lift the Utility in Privacy-Preserving ERM

Paper content:
Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones.
To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) to train ML models with differential privacy (DP) guarantees.
At the core of DP-LSSGD is the Laplacian smoothing, which smooths out the Gaussian noise used in the Gaussian mechanism.
Under the same amount of noise used in the Gaussian mechanism, DP-LSSGD attains the same DP guarantee, but a better utility especially for the scenarios with strong DP guarantees.
In practice, DP-LSSGD makes training both convex and nonconvex ML models more stable and enables the trained models to generalize better.
The proposed algorithm is simple to implement and the extra computational complexity and memory overhead compared with DP-SGD are negligible.
DP-LSSGD is applicable to train a large variety of ML models, including DNNs.

assistant: text: We propose a differentially private Laplacian smoothing stochastic gradient descent to train machine learning models with better utility and maintain differential privacy guarantees.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Guiding Physical Intuition with Neural Stethoscopes

Paper content:
Model interpretability and systematic, targeted model adaptation present central challenges in deep learning.
In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning.
Our contributions are two-fold.
Firstly, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate.
In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses.
Secondly, we deploy the stethoscope framework to provide an in-depth analysis of a state-of-the-art deep neural network for stability prediction, specifically examining its physical reasoning.
We show that the baseline model is susceptible to being misled by incorrect visual cues.
This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability.
Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy.
Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset.
Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.

assistant: text: Combining auxiliary and adversarial training to interrogate and help physical understanding.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Learning effective text representations is a key foundation for numerous machine learning and NLP applications.
While the celebrated Word2Vec technique yields semantically rich word representations, it is less clear whether sentence or document representations should be built upon word representations or from scratch.
Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy.
However, WMD is very expensive to compute, and is harder to apply beyond simple KNN than feature embeddings.
In this paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings.
Our technique extends the theory of \emph{Random Features} to show convergence of the inner product between WMEs to a positive-definite kernel that can be interpreted as a soft version of (inverse) WMD.
The proposed embedding is more efficient and flexible than WMD in many situations.
As an example, WME with a simple linear classifier reduces the computational cost of WMD-based KNN \emph{from cubic to linear} in document length and \emph{from quadratic to linear} in number of samples, while simultaneously improving accuracy.
In experiments on 9 benchmark text classification datasets and 22 textual similarity tasks the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.

assistant: text: A novel approach to building an unsupervised document (sentence) embeddings from pre-trainedword embeddings
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automated Animal Training and Iterative Inference of Latent Learning Policy

Paper content:
Progress in understanding how individual animals learn requires high-throughput standardized methods for behavioral training and ways of adapting training.
During the course of training with hundreds or thousands of trials, an animal may change its underlying strategy abruptly, and capturing these changes requires real-time inference of the animal’s latent decision-making strategy.
To address this challenge, we have developed an integrated platform for automated animal training, and an iterative decision-inference model that is able to infer the momentary decision-making policy, and predict the animal’s choice on each trial with an accuracy of ~80\%, even when the animal is performing poorly.
We also combined decision predictions at single-trial resolution with automated pose estimation to assess movement trajectories.
Analysis of these features revealed categories of movement trajectories that associate with decision confidence.

assistant: text: Automated mice training for neuroscience with online iterative latent strategy inference for behavior prediction
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Open Loop Hyperparameter Optimization and Determinantal Point Processes

Paper content:
Driven by the need for parallelizable hyperparameter optimization methods, this paper studies open loop search methods: sequences that are predetermined and can be generated before a single configuration is evaluated.
Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.
In particular, we propose the use of k-determinantal point processes in  hyperparameter optimization via random search.
Compared to conventional uniform random search where hyperparameter settings are sampled independently, a k-DPP promotes diversity.
We describe an approach that transforms hyperparameter search spaces for efficient use with a k-DPP.
In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from k-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure.
Our experiments show significant benefits  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.

assistant: text: We address fully parallel hyperparameter optimization with Determinantal Point Processes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Leveraging Static and Contextualized Embeddings for Word Alignments

Paper content:
Word alignments are useful for tasks like statistical and neural machine translation (NMT) and annotation projection.
Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT.
However, most approaches require parallel training data and quality decreases as less training data is available.
We propose word alignment methods that require little or no parallel data.
The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment.
Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries.
We find that traditional statistical aligners are outperformed by contextualized embeddings – even in scenarios with abundant parallel data.
For example, for a set of 100k parallel sentences, contextualized embeddings achieve a word alignment F1 that is more than 5% higher (absolute) than eflomal.

assistant: text: We use representations trained without any parallel data for creating word alignments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Authorship Verification with Transfer Learning

Paper content:
We address the problem of open-set authorship verification, a classification task that consists of attributing texts of unknown authorship to a given author when the unknown documents in the test set are excluded from the training set.
We present an end-to-end model-building process that is universally applicable to a wide variety of corpora with little to no modification or fine-tuning.
It relies on transfer learning of a deep language model and uses a generative adversarial network and a number of text augmentation techniques to improve the model's generalization ability.
The language model encodes documents of known and unknown authorship into a domain-invariant space, aligning document pairs as input to the classifier, while keeping them separate.
The resulting embeddings are used to train to an ensemble of recurrent and quasi-recurrent neural networks.
The entire pipeline is bidirectional; forward and backward pass results are averaged.
We perform experiments on four traditional authorship verification datasets, a collection of machine learning papers mined from the web, and a large Amazon-Reviews dataset.
Experimental results surpass baseline and current state-of-the-art techniques, validating the proposed approach.

assistant: text: We propose and end-to-end model-building process that is universally applicable to a wide variety of authorship verification corpora and outperforms state-of-the-art with little to no modification or fine-tuning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature Intertwiner for Object Detection

Paper content:
A well-trained model should classify objects with unanimous score for every category.
This requires the high-level semantic features should be alike among samples, despite a wide span in resolution, texture, deformation, etc.
Previous works focus on re-designing the loss function or proposing new regularization constraints on the loss.
In this paper, we address this problem via a new perspective.
For each category, it is assumed that there are two sets in the feature space: one with more reliable information and the other with less reliable source.
We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher’s behavior and thus pushing towards a more compact class centroid in the high-dimensional space.
Such a scheme also benefits the reliable set since samples become more closer within the same category - implying that it is easilier for the classifier to identify.
We refer to this mutual learning process as feature intertwiner and embed the spirit into object detection.
It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass.
We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set.
Specifically, an intertwiner is achieved by minimizing the distribution divergence between two sets.
We design a historical buffer to represent all previous samples in the reliable set and utilize them to guide the feature learning of the less reliable set.
The design of obtaining an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) algorithm into the framework.
Samples in the less reliable set are better aligned with the reliable set with aid of OT metric.
Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts on the COCO object detection benchmark.

assistant: text: (Camera-ready version) A feature intertwiner module to leverage features from one accurate set to help the learning of another less reliable set.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy  on Limited Data

Paper content:
We seek to auto-generate stronger  input features  for ML methods faced with limited training data.
Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features.
In particular, the insect olfactory network  learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights.
In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator.
Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers.
These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%.
The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs.
These results highlight the potential value of BNN-inspired feature generators in the ML context.

assistant: text: Features auto-generated by the bio-mimetic MothNet model significantly improve the test accuracy of standard ML methods on vectorized MNIST. The MothNet-generated features also outperform standard feature generators.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning

Paper content:
Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go).
In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration.
These algorithms iteratively solve short-horizon decision problems and converge to the optimal solution of the original one.
By using model-free algorithms as solvers of the short-horizon problems we derive fully model-free algorithms which are instances of the multi-step DP framework.
As model-free algorithms are prone to instabilities w.r.t. the decision problem horizon, this simple approach can help in mitigating these instabilities and results in an improved model-free algorithms.
We test this approach and show results on both discrete and continuous control problems.

assistant: text: Use model free algorithms like DQN/TRPO to solve short horizon problems (model free) iteratively in a Policy/Value Iteration fashion.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Boosting the Actor with Dual Critic

Paper content:
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.
It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic.
Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor.
We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm.
We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.

assistant: text: We propose Dual Actor-Critic algorithm, which is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation. The algorithm achieves the state-of-the-art performances across several benchmarks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A  Model Cortical Network for Spatiotemporal Sequence Learning and Prediction

Paper content:
In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet) to understand how spatiotemporal memories might be learned and encoded in a representational hierarchy for predicting future video frames.
The model is inspired by the feedforward, feedback and lateral recurrent circuits in the mammalian hierarchical visual system.
It assumes that spatiotemporal memories are encoded in the recurrent connections within each level and between different levels of the hierarchy.
The model contains a feed-forward path that  computes and encodes spatiotemporal features of successive complexity and a feedback path that projects interpretation from a higher level to the level below.
Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit that integrates their signals as well as the circuit's internal memory states to generate  a prediction of the incoming signals.
The network learns by comparing the incoming signals with its prediction, updating its internal model of the world by minimizing the prediction errors at each level of the hierarchy in the style of {\em predictive self-supervised learning}. The network processes data in blocks of video  frames rather than  a frame-to-frame basis.
This allows it to learn relationships among movement patterns, yielding state-of-the-art performance in long range video sequence predictions in benchmark datasets.
We observed that hierarchical interaction in the network introduces sensitivity to memories of global movement patterns even in the population representation of the units in the earliest level.
Finally, we provided neurophysiological evidence, showing that neurons in the early visual cortex of awake monkeys exhibit very similar sensitivity and behaviors.
These findings suggest that  predictive self-supervised learning might be an important principle for representational learning in the visual cortex.

assistant: text: A new hierarchical cortical model for encoding spatiotemporal memory and video prediction
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PixelNN: Example-based Image Synthesis

Paper content:
We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges.
Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem.
(2) they are not interpretable, making it difficult to control the synthesized output.
We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets.
We design a simple pipeline that combines the best of both worlds:  the first stage uses a convolutional neural network (CNN) to map the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner.
Importantly, pixel-wise matching allows our method to compose novel high-frequency content by cutting-and-pasting pixels from different training exemplars.
We demonstrate our approach for various input modalities, and for various domains ranging from human faces, pets, shoes, and handbags.

assistant: text: Pixel-wise nearest neighbors used for generating multiple images from incomplete priors such as a low-res images, surface normals, edges etc.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Principled Deep Neural Network Training through Linear Programming

Paper content:
Deep Learning has received significant attention due to its impressive performance in many state-of-the-art learning tasks.
Unfortunately, while very powerful, Deep Learning is not well understood theoretically and in particular only recently results for the complexity of training deep neural networks have been obtained.
In this work we show that large classes of deep neural networks with various architectures (e.g., DNNs, CNNs, Binary Neural Networks, and ResNets), activation functions (e.g., ReLUs and leaky ReLUs), and loss functions (e.g., Hinge loss, Euclidean loss, etc) can be trained to near optimality with desired target accuracy using linear programming in time that is exponential in the input data and parameter space dimension and polynomial in the size of the data set; improvements of the dependence in the input dimension are known to be unlikely assuming $P\neq NP$, and improving the dependence on the parameter space dimension remains open.
In particular, we obtain polynomial time algorithms for training for a given fixed network architecture.
Our work applies more broadly to empirical risk minimization problems which allows us to generalize various previous results and obtain new complexity results for previously unstudied architectures in the proper learning setting.

assistant: text: Using linear programming we show that the computational complexity of approximate Deep Neural Network training depends polynomially on the data size for several architectures
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Control PDEs with Differentiable Physics

Paper content:
Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning.
A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom.
Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters.
We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames.
We propose to split the problem into two distinct tasks: planning and control.
To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters.
Both stages are trained end-to-end using a differentiable PDE solver.
We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.

assistant: text: We train a combination of neural networks to predict optimal trajectories for complex physical systems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How to make someone speak a language that they don't know.

Paper content:
We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.
These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing.
We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.
Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron.
The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages.
This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker.
Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.

assistant: text: We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparse Networks from Scratch: Faster Training without Losing Performance

Paper content:
We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels.
We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently.
Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer.
Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights.
We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms.
Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training.
In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network.

assistant: text: Redistributing and growing weights according to the momentum magnitude enables the training of sparse networks from random initializations that can reach dense performance levels with 5% to 50% weights while accelerating training by up to 5.6x.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LAMOL: LAnguage MOdeling for Lifelong Language Learning

Paper content:
Most research on lifelong learning applies to images or games, but not language.
We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.
LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.
Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples.
When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task.
The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model.
Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound.
The source code is available at https://github.com/jojotenya/LAMOL.

assistant: text: Language modeling for lifelong language learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning with Little Data: Evaluation of Deep Learning Algorithms

Paper content:
Deep learning has become a widely used tool in many computational and classification problems.
Nevertheless obtaining and labeling data, which is needed for strong results, is often expensive or even not possible.
In this paper three different algorithmic approaches to deal with limited access to data are evaluated and compared to each other.
We show the drawbacks and benefits of each method.
One successful approach, especially in one- or few-shot learning tasks, is the use of external data during the classification task.
Another  successful approach, which achieves state of the art results in semi-supervised learning (SSL) benchmarks, is consistency regularization.
Especially virtual adversarial training (VAT) has shown strong results and will be investigated in this paper.
The aim of consistency regularization is to force the network not to change the output, when the input or the network itself is perturbed.
Generative adversarial networks (GANs) have also shown strong empirical results.
In many approaches the GAN architecture is used in order to create additional data and therefor to increase the generalization capability of the classification network.
Furthermore we consider the use of unlabeled data for further performance improvement.
The use of unlabeled data is investigated both for GANs and VAT.

assistant: text: Comparison of siamese neural networks, GANs, and VAT for few shot learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Mahalanobis Metric Spaces via Geometric Approximation Algorithms

Paper content:
Learning Mahalanobis metric spaces is an important problem that has found numerous applications.
Several algorithms have been designed for this problem, including Information Theoretic Metric Learning (ITML) [Davis et al. 2007] and Large Margin Nearest Neighbor (LMNN) classification [Weinberger and Saul 2009].
We consider a formulation of Mahalanobis metric learning as an optimization problem,where the objective is to minimize the number of violated similarity/dissimilarity constraints.
We show that for any fixed ambient dimension, there exists a fully polynomial time approximation scheme (FPTAS) with nearly-linear running time.
This result is obtained using tools from the theory of linear programming in low dimensions.
We also discuss improvements of the algorithm in practice, and present experimental results on synthetic and real-world data sets.
Our algorithm is fully parallelizable and performs favorably in the presence of adversarial noise.

assistant: text: Fully parallelizable and adversarial-noise resistant metric learning algorithm with theoretical guarantees.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Sequential Prediction of Continuous Actions for Deep RL

Paper content:
It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned.
In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces.
Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time.
Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions.
With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately).
On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG.
We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.

assistant: text: A method to do Q-learning on continuous action spaces by predicting a sequence of discretized 1-D actions.