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

Title: Scalable Gradients and Variational Inference for Stochastic Differential Equations

Paper content:
We derive reverse-mode (or adjoint) automatic differentiation for solutions of stochastic differential equations (SDEs), allowing time-efficient and constant-memory computation of pathwise gradients, a continuous-time analogue of the reparameterization trick.
Specifically, we construct a backward SDE whose solution is the gradient and provide conditions under which numerical solutions converge.
We also combine our stochastic adjoint approach with a stochastic variational inference scheme for continuous-time SDE models, allowing us to learn distributions over functions using stochastic gradient descent.
Our latent SDE model achieves competitive performance compared to existing approaches on time series modeling.
Deterministic dynamical systems can often be modeled by ordinary differential equations (ODEs).
For training, a memory-efficient implementation of the adjoint sensitivity method (Chen et al., 2018) effectively computes gradients through ODE solutions with constant memory cost.
Stochastic differential equations (SDEs) are a generalization of ODEs which incorporate instantaneous noise into their dynamics (Arnold, 1974; Øksendal, 2003) .
They are a natural fit for modeling phenomena governed by small and unobserved interactions.
In this paper, we generalize the adjoint method to dynamics defined by SDEs resulting in an approach which we call the stochastic adjoint sensitivity method.
Building on theoretical advances by Kunita (2019), we derive a memory-efficient adjoint method whereby we simultaneously reconstruct the original trajectory and evaluate the gradients by solving a backward SDE (in the sense of Kunita (2019)) whose formulation we detail in Section 3.
Computationally, in order to retrace the original trajectory during the backward pass, we need to reuse noise samples generated in the forward pass.
In Section 4, we give an algorithm that allows arbitrarily-precise querying of a Brownian motion realization at any time point, while only storing a single random seed.
Overall, this results in a constant-memory algorithm that approximates the gradient arbitrarily well as step size reduces by computing vector-Jacobian products a constant number of times per-iteration.
See Table 2 for a comparison of our method against previous approaches in terms of asymptotic time and memory complexity.
We incorporate SDEs into a stochastic variational inference framework, whereby we efficiently compute likelihood ratios and backpropagate through the evidence lower bound using our adjoint approach.
This effectively generalizes existing model families such as latent ODEs (Rubanova et al., 2019) and deep Kalman filters (Krishnan et al., 2017) .

assistant: text: We present a constant memory gradient computation procedure through solutions of stochastic differential equations (SDEs) and apply the method for learning latent SDE models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Message Passing for Multi-Label Classification

Paper content:
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample.
Modeling the combinatorial label interactions in MLC has been a long-haul challenge.
Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC.
However, the sequential nature of modeling label dependencies through an RNN limits its ability in parallel computation, predicting dense labels, and providing interpretable results.
In this paper, we propose Message Passing Encoder-Decoder (MPED) Networks,  aiming to provide fast, accurate, and interpretable MLC.
MPED networks model the joint prediction of labels by replacing all RNNs in the encoder-decoder architecture with message passing mechanisms and dispense with autoregressive inference entirely.
The proposed models are simple, fast, accurate, interpretable, and structure-agnostic (can be used on known or unknown structured data).
Experiments on seven real-world MLC datasets show the proposed models outperform autoregressive RNN models across five different metrics with a significant speedup during training and testing time.

assistant: text: We propose Message Passing Encoder-Decode networks for a fast and accurate way of modelling label dependencies for multi-label classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Reinforcement Learning for Autonomous Driving 

Paper content:
Autonomous driving is still considered as an “unsolved problem” given its inherent important variability and that many processes associated with its development like vehicle control and scenes recognition remain open issues.
Despite reinforcement learning algorithms have achieved notable results in games and some robotic manipulations, this technique has not been widely scaled up to the more challenging real world applications like autonomous driving.
In this work, we propose a deep reinforcement learning (RL) algorithm embedding an actor critic architecture with multi-step returns to achieve a better robustness of the agent learning strategies when acting in complex and unstable environments.
The experiment is conducted with Carla simulator offering a customizable and realistic urban driving conditions.
The developed deep actor RL guided by a policy-evaluator critic distinctly surpasses the performance of a standard deep RL agent.

assistant: text: An actor-critic reinforcement learning approach with multi-step returns applied to autonomous driving with Carla simulator.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization

Paper content:
Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.
When its activation function is linear and the encoding dimension (width of hidden layer) is smaller than the input dimension, it is well known that auto-encoder is optimized to learn the principal components of the data distribution (Oja1982).
However, when the activation is nonlinear and when the width is larger than the input dimension (overcomplete), auto-encoder behaves differently from PCA, and in fact is known to perform well empirically for sparse coding problems.
We provide a theoretical explanation for this empirically observed phenomenon, when rectified-linear unit (ReLu) is adopted as the activation function and the hidden-layer width is set to be large.
In this case, we show that, with significant probability, initializing the weight matrix of an auto-encoder by sampling from a spherical Gaussian distribution followed by stochastic gradient descent (SGD) training converges towards the ground-truth representation for a class of sparse dictionary learning models.
In addition, we can show that, conditioning on convergence, the expected convergence rate is O(1/t), where t is the number of updates.
Our analysis quantifies how increasing hidden layer width helps the training performance when random initialization is used, and how the norm of network weights influence the speed of SGD convergence.

assistant: text: theoretical analysis of nonlinear wide autoencoder
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION

Paper content:
Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models.
Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset,  which is very computationally expensive.
The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors.
However, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks.
Existing solutions for large batch training either do not work or require massive hyper-parameter tuning.
To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against ``sharp minima'') and second order optimization (to use curvature information to change batch size adaptively during training).
We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as compressed networks such as SqueezeNext.
Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $3\times$, respectively).
We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our method to any of these experiments.

assistant: text: Large batch size training using adversarial training and second order information
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FlowQA: Grasping Flow in History for Conversational Machine Comprehension

Paper content:
Conversational machine comprehension requires a deep understanding of the conversation history.
To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure.
Compared to shallow approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply.
Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC).
The effectiveness of Flow also shows in other tasks.
By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Q3
We presented a novel FLOW component for conversational machine comprehension.
By applying FLOW to a state-of-the-art machine comprehension model, our model encodes the conversation history more comprehensively, and thus yields better performance.
When evaluated on two recently proposed conversational challenge datasets and three domains of a sequential instruction understanding task (through reduction), FLOWQA outperforms existing models.While our approach provides a substantial performance gain, there is still room for improvement.
In the future, we would like to investigate more efficient and fine-grained ways to model the conversation flow, as well as methods that enable machines to engage more active and natural conversational behaviors, such as asking clarification questions.

assistant: text: We propose the Flow mechanism and an end-to-end architecture, FlowQA, that achieves SotA on two conversational QA datasets and a sequential instruction understanding task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$

Paper content:
In recent years several adversarial attacks and defenses have been proposed.
Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used.
One way out of this dilemma are provable robustness guarantees.
While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations.
We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \textit{and} $l_\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\geq 1$.

assistant: text: We introduce a method to train models with provable robustness wrt all the $l_p$-norms for $p\geq 1$ simultaneously.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feat2Vec:  Dense Vector Representation for Data with Arbitrary Features

Paper content:
Methods that calculate dense vector representations for features in unstructured data—such as words in a document—have proven to be very successful for knowledge representation.
We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit labels are available, as well as for unsupervised learning where there are no labels.
Feat2Vec calculates embeddings for data with multiple feature types enforcing that all different feature types exist in a common space.
In the supervised case, we show that our method has advantages over recently proposed methods; such as enabling higher prediction accuracy, and providing a way to avoid the cold-start
problem.
In the unsupervised case, our experiments suggest that Feat2Vec significantly outperforms existing algorithms that do not leverage the structure of the data.
We believe that we are the first to propose a method for learning unsuper vised embeddings that leverage the structure of multiple feature types.

assistant: text: Learn dense vector representations of arbitrary types of features in labeled and unlabeled datasets
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Language Agnostic Universal Representations

Paper content:
When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language.
However, current representations in machine learning are language dependent.
In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion.
We learn these representations by taking inspiration from linguistics, specifically the Universal Grammar hypothesis and learn universal latent representations that are language agnostic (Chomsky, 2014; Montague, 1970).
We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.

assistant: text: By taking inspiration from linguistics, specifically the Universal Grammar hypothesis, we learn language agnostic universal representations which we can utilize to do zero-shot learning across languages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Polar Transformer Networks

Paper content:
Convolutional neural networks (CNNs) are inherently equivariant to translation.
Efforts to embed other forms of equivariance have concentrated solely on rotation.
We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN).
PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations.
The result is a network invariant to translation and equivariant to both rotation and scale.
PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier.
PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling.
The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.

assistant: text: We learn feature maps invariant to translation, and equivariant to rotation and scale.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation

Paper content:
Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media.
When it comes to visualising these large corpora, either the textual content or the network graph are used.
In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

assistant: text: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Confidence of Neural Network Predictions for some NLP Tasks

Paper content:
Neural networks are known to produce unexpected results on inputs that are far from the training distribution.
One approach to tackle this problem is to detect the samples on which the trained network can not answer reliably.
ODIN is a recently proposed method for out-of-distribution detection that does not modify the trained network and achieves good performance for various image classification tasks.
In this paper we adapt ODIN for sentence classification and word tagging tasks.
We show that the scores produced by ODIN can be used as a confidence measure for the predictions on both in-distribution and out-of-distribution datasets.

assistant: text: A recent out-of-distribution detection method helps to measure the confidence of RNN predictions for some NLP tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisiting Reweighted Wake-Sleep

Paper content:
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.
Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons:
1) branching on the samples within the model, and
2) the lack of a pathwise derivative for the samples.
While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model.
Here, we revisit the Reweighted Wake Sleep (RWS; Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models.
Moreover, we observe that, unlike the Importance-weighted Autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well.
Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.

assistant: text: Empirical analysis and explanation of particle-based gradient estimators for approximate inference with deep generative models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hiding Objects from Detectors: Exploring Transferrable Adversarial Patterns

Paper content:
Adversaries in neural networks have drawn much attention since their first debut.
While most existing methods aim at deceiving image classification models into misclassification or crafting attacks for specific object instances in the object setection tasks, we focus on creating universal adversaries to fool object detectors and hide objects from the detectors.
The adversaries we examine are universal in three ways:
(1) They are not specific for specific object instances;
(2) They are image-independent;
(3) They can further transfer to different unknown models.
To achieve this, we propose two novel techniques to improve the transferability of the adversaries: \textit{piling-up} and \textit{monochromatization}.
Both techniques prove to simplify the patterns of generated adversaries, and ultimately result in higher transferability.

assistant: text: We focus on creating universal adversaries to fool object detectors and hide objects from the detectors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Audio Synthesis

Paper content:
Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.
Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation.
In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio.
WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation.
Our experiments demonstrate that—without labels—WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano.
We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.

assistant: text: Learning to synthesize raw waveform audio with GANs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Cross-lingual Embeddings from Parallel Sentences 

Paper content:
Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation.
However, these approaches assume word embedding spaces are isomorphic between different languages, which has been shown not to  hold in practice (Søgaard et al., 2018), and fundamentally limits their performance.
This motivates investigating joint learning methods which can overcome this impediment, by simultaneously learning embeddings across languages via a cross-lingual term in the training objective.
Given the abundance of parallel data available (Tiedemann, 2012), we propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word and sentence representations.
Our approach significantly improves cross-lingual sentence retrieval performance over all other approaches, as well as convincingly outscores mapping methods while maintaining parity with jointly trained methods on word-translation.
It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task, requiring far fewer computational resources for training and inference.
As an additional advantage, our bilingual method also improves the quality of monolingual word vectors despite training on much smaller datasets.
We make our code and models publicly available.

assistant: text: Joint method for learning cross-lingual embeddings with state-of-art performance for cross-lingual tasks and mono-lingual quality
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.
The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods.
Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators.
These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects.
In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects.
The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal.
Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines.

assistant: text: Learning compositional Koopman operators for efficient system identification and model-based control.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: StrokeNet: A Neural Painting Environment

Paper content:
We've seen tremendous success of image generating models these years.
Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes.
To imitate human drawing, interactions between the environment and the agent is required to allow trials.
However, the environment is usually non-differentiable, leading to slow convergence and massive computation.
In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation.
We present  StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment.
With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner.
Our primary contribution is the neural simulation of a real-world environment.
Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.

assistant: text: StrokeNet is a novel architecture where the agent is trained to draw by strokes on a differentiable simulation of the environment, which could effectively exploit the power of back-propagation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps

Paper content:
We present and discuss a simple image preprocessing method for learning disentangled latent factors.
In particular, we utilize the implicit inductive bias contained in features from networks pretrained on the ImageNet database.
We enhance this bias by explicitly fine-tuning such pretrained networks on tasks useful for the NeurIPS2019 disentanglement challenge, such as angle and position estimation or color classification.
Furthermore, we train a VAE on regionally aggregate feature maps, and discuss its disentanglement performance using metrics proposed in recent literature.

assistant: text: We use supervised finetuning of feature vectors to improve transfer from simulation to the real world
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mutual Exclusivity as a Challenge for Deep Neural Networks

Paper content:
Strong inductive biases allow children to learn in fast and adaptable ways.
Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another.
In this paper, we investigate whether or not standard neural architectures have a ME bias, demonstrating that they lack this learning assumption.
Moreover, we show that their inductive biases are poorly matched to lifelong learning formulations of classification and translation.
We demonstrate that there is a compelling case for designing neural networks that reason by mutual exclusivity, which remains an open challenge.

assistant: text: Children use the mutual exclusivity (ME) bias to learn new words, while standard neural nets show the opposite bias, hindering learning in naturalistic scenarios such as lifelong learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Exploration via State Marginal Matching

Paper content:
Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them.
The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being optimized by existing exploration methods, or how they can be altered to incorporate prior knowledge about the task.
Moreover, it is unclear how to acquire a single exploration strategy that will be useful for solving multiple downstream tasks.
We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore.
We recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task.
We optimize the objective by reducing it to a two-player, zero-sum game between a state density model and a parametric policy.
Our theoretical analysis of this approach suggests that prior exploration methods do not learn a policy that does distribution matching, but acquire a replay buffer that performs distribution matching, an observation that potentially explains these prior methods' success in single-task settings.
On both simulated and real-world tasks, we demonstrate that our algorithm explores faster and adapts more quickly than prior methods.

assistant: text: We view exploration in RL as a problem of matching a marginal distribution over states.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed

Paper content:
Stochastic gradient descent (SGD), which  trades off noisy gradient updates for computational efficiency, is the de-facto optimization algorithm to solve large-scale machine learning problems.
SGD can make rapid learning progress by performing updates using subsampled training data, but the noisy updates also lead to slow asymptotic convergence.
Several variance reduction algorithms, such as SVRG, introduce control variates to obtain a lower variance gradient estimate and faster convergence.
Despite their appealing asymptotic guarantees, SVRG-like algorithms have not been widely adopted in deep learning.
The traditional asymptotic analysis in stochastic optimization provides limited insight into training deep learning models under a fixed number of epochs.
In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem.
Our primary focus is to compare the exact loss of SVRG to that of SGD at each iteration t.
We show that the learning dynamics of our regression model closely matches with that of neural networks on MNIST and CIFAR-10 for both the underparameterized and the overparameterized models.
Our analysis and experimental results suggest there is a trade-off between the computational cost and the convergence speed in underparametrized neural networks.
SVRG outperforms SGD after a few epochs in this regime.
However, SGD is shown to always outperform SVRG in the overparameterized regime.

assistant: text: Non-asymptotic analysis of SGD and SVRG, showing the strength of each algorithm in convergence speed and computational cost, in both under-parametrized and over-parametrized settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs

Paper content:
We introduce a neural network that represents sentences by composing their words according to induced binary parse trees.
We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser.
Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM.
It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees.
As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation.
We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task.
Finally, we show how performance can be improved with an attention mechanism which fully exploits the parse chart, by attending over all possible subspans of the sentence.

assistant: text: Represent sentences by composing them with Tree-LSTMs according to automatically induced parse trees.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sinkhorn Permutation  Variational Marginal Inference

Paper content:
We address the problem of marginal inference for an exponential family defined over the set of permutation matrices.
This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.
We introduce Sinkhorn variational marginal inference as a scalable alternative, a method whose validity is ultimately justified by the so-called Sinkhorn approximation of the permanent.
We demonstrate the efectiveness of our method in the problem of probabilistic identification of neurons in the worm C.elegans
Let P ∈ R n×n be a binary matrix representing a permutation of n elements (i.e. each row and column of P contains a unique 1).
We consider the distribution over P defined as
where A, B F is the Frobenius matrix inner product, log L is a parameter matrix and Z L is the normalizing constant.
Here we address the problem of marginal inference, i.e. computing the matrix of expectations ρ := E(P).
This problem is known to be intractable since it requires access to Z L , also known as the permanent of L, and whose computation is known to be a #P-hard problem Valiant (1979) To overcome this difficulty we introduce Sinkhorn variational marginal inference, which can be computed efficiently and is straightforward to implement.
Specifically, we approximate ρ as S(L), the Sinkhorn operator applied to L (Sinkhorn, 1964) .
S(L) is defined as the (infinite) successive row and column normalization of L (Adams and Zemel, 2011; , a limit that is known to result in a doubly stochastic matrix (Altschuler et al., 2017) .
In section 2 we argue the Sinkhorn approximation is sensible, and in section 3 we describe the problem of probabilistic inference of neural identity in C.elegans and demonstrate the Sinkhorn approximation produces the best results.
We have introduced the Sinkhorn approximation for marginal inference, and our it is a sensible alternative to sampling, and it may provide faster, simpler and more accurate approximate marginals than the Bethe approximation, despite typically leading to worse permanent approximations.
We leave for future work a thorough analysis of the relation between quality of permanent approximation and corresponding marginals.
Also, it can be verified that S(L) = diag(x)Ldiag(y
), where
diag(x), diag(y
) are some
positive vectors x, y turned into diagonal matrices (Peyré et al., 2019) . Then,
Additionally, we obtain the (log) Sinkhorn approximation of the permanent of L, perm S (L), by evaluating S(L) in the problem it solves, (2.3).
By simple algebra and using the fact that S(L) is a doubly stochastic matrix we see that
By combining the last three displays we obtain
from which the result follows.

assistant: text: New methodology for variational marginal inference of permutations based on Sinkhorn algorithm, applied to probabilistic identification of neurons
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SMiRL: Surprise Minimizing RL in Entropic Environments

Paper content:
All living organisms struggle against the forces of nature to carve out niches where
they can maintain relative stasis.
We propose that such a search for order amidst
chaos might offer a unifying principle for the emergence of useful behaviors in
artificial agents.
We formalize this idea into an unsupervised reinforcement learning
method called surprise minimizing RL (SMiRL).
SMiRL trains an agent with the
objective of maximizing the probability of observed states under a model trained on
all previously seen states.
The resulting agents acquire several proactive behaviors
to seek and maintain stable states such as balancing and damage avoidance, that
are closely tied to the affordances of the environment and its prevailing sources
of entropy, such as winds, earthquakes, and other agents.
We demonstrate that
our surprise minimizing agents can successfully play Tetris, Doom, and control
a  humanoid  to  avoid  falls,  without  any  task-specific  reward  supervision.
We
further show that SMiRL can be used as an unsupervised pre-training objective
that substantially accelerates subsequent reward-driven learning

assistant: text: Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FSPool: Learning Set Representations with Featurewise Sort Pooling

Paper content:
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem.
We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set.
This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem.
On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations.
Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.

assistant: text: Sort in encoder and undo sorting in decoder to avoid responsibility problem in set auto-encoders
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning protein sequence embeddings using information from structure

Paper content:
Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology.
Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function.
Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins.
We newly approach this problem through the lens of representation learning.
We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information.
We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from
(i) global structural similarity between proteins and
(ii) pairwise residue contact maps for individual proteins.
To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them.
Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences.
We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal.
Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.

assistant: text: We present a method for learning protein sequence embedding models using structural information in the form of global structural similarity between proteins and within protein residue-residue contacts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization.
Despite plenty of prior work on the quantization of weights or activations for neural networks, there is still a wide gap between the software quantizers and the low-precision accelerator implementation, which degrades either the efficiency of networks or that of the hardware for the lack of software and hardware coordination at design-phase.
In this paper, we propose a learned linear symmetric quantizer for integer neural network processors, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators (e.g., 16-bit) and multipliers (e.g., 4-bit).
We use a unified way to quantize weights and activations, and the results outperform many previous approaches for various networks such as AlexNet, ResNet, and lightweight models like MobileNet while keeping friendly to the accelerator architecture.
Additional, we also apply the method to object detection models and witness high performance and accuracy in YOLO-v2.
Finally, we deploy the quantized models on our specialized integer-arithmetic-only DNN accelerator to show the effectiveness of the proposed quantizer.
We show that even with linear symmetric quantization, the results can be better than asymmetric or non-linear methods in 4-bit networks.
In evaluation, the proposed quantizer induces less than 0.4\% accuracy drop in ResNet18, ResNet34, and AlexNet when quantizing the whole network as required by the integer processors.

assistant: text: We introduce an efficient quantization process that allows for performance acceleration on specialized integer-only neural network accelerator.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving image generative models with human interactions

Paper content:
GANs provide a framework for training generative models which mimic a data distribution.
However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images.
In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction.
Here, we develop a system for efficiently training a GAN to increase a generic rate of positive user interactions, for example aesthetic ratings.
To do this, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model.
As a proof of concept, we demonstrate that this system is successful at improving positive interaction rates simulated from a variety of objectives, and characterize s

assistant: text: We describe how to improve an image generative model according to a slow- or difficult-to-evaluate objective, such as human feedback, which could have many applications, like making more aesthetic images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes

Paper content:
Partially observable Markov decision processes (POMDPs) are a widely-used framework to model decision-making with uncertainty about the environment and under stochastic outcome.
In conventional POMDP models, the observations that the agent receives originate from fixed known distribution.
However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive.
Due to combinatorial nature of such selection process, it is computationally intractable to integrate the perception decision with the planning decision.
To prevent such expansion of the action space, we propose a greedy strategy for observation selection that aims to minimize the uncertainty in state.
We develop a novel point-based value iteration algorithm that incorporates the greedy strategy to achieve near-optimal uncertainty reduction for sampled belief points.
This in turn enables the solver to efficiently approximate the reachable subspace of belief simplex by essentially separating computations related to perception from planning.
Lastly, we implement the proposed solver and demonstrate its performance and computational advantage in a range of robotic scenarios where the robot simultaneously performs active perception and planning.

assistant: text: We develop a point-based value iteration solver for POMDPs with active perception and planning tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data-dependent Gaussian Prior Objective for Language Generation

Paper content:
For typical sequence prediction problems such as language generation, maximum likelihood estimation (MLE) has commonly been adopted as it encourages the predicted sequence most consistent with the ground-truth sequence to have the highest probability of occurring.
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
We refer to this drawback as {\it negative diversity ignorance} in this paper.
Treating all incorrect predictions as equal unfairly downplays the nuance of these sequences' detailed token-wise structure.
To counteract this, we augment the MLE loss by introducing an extra Kullback--Leibler divergence term derived by comparing a data-dependent Gaussian prior and the detailed training prediction.
The proposed data-dependent Gaussian prior objective (D2GPo) is defined over a prior topological order of tokens and is poles apart from the data-independent Gaussian prior (L2 regularization) commonly adopted in smoothing the training of MLE.
Experimental results show that the proposed method makes effective use of a more detailed prior in the data and has improved performance in typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image captioning.

assistant: text: We introduce an extra data-dependent Gaussian prior objective to augment the current MLE training, which is designed to capture the prior knowledge in the ground-truth data.