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

Title: Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning

Paper content:
In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability.
Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL).
This work shows that a differentiable activation function is not necessary any more for error backpropagation.
The derivative of the activation function can be replaced by an iterative temporal differencing (ITD) using fixed random feedback weight alignment (FBA).
Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures.
We don't claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning.
VBP was proposed around 1987 BID10 .
Almost at the same time, biologicallyinspired convolutional networks was also introduced as well using VBP BID5 .
Deep learning (DL) was introduced as an approach to learn deep neural network architecture using VBP BID5 ; BID4 .
Extremely deep networks learning reached 152 layers of representation with residual and highway networks BID3 ; BID13 .
Deep reinforcement learning was successfully implemented and applied which was mimicking the dopamine effect in our brain for self-supervised and unsupervised learning BID11 BID9 BID8 .
Hierarchical convolutional neural network have been biologically inspired by our visual cortex Hubel & Wiesel (1959) ; BID1 BID0 BID14 .
The discovery of fixed random synaptic feedback weights alignments (FBA) in error backpropagation for deep learning started a new quest of finding the biological version of VBP since it solves the symmetrical synaptic weights problem in backprop.
Recently, spiketime dependent plasticity was the important issue with backprop.
One of the works in this direction, highly inspired from Hinton's recirculation idea Hinton & McClelland (1988) , is deep learning using segregated dendrites BID2 .
Apical dendrites as the segregated synaptic feedback are claimed to be capable of modeling STDP into the backprop successfully BID2 .
In this paper, we took one more step toward a more biologically plausible backpropagation for deep learning.
After hierarchical convolutional neural network and fixed random synaptic feedback alignment, we believe iterative temporal differencing is a way toward integrating STDP learning process in the brain.
We believe the next steps should be to investigate more into the STDP processes details in learning, dopamine-based unsupervised learning, and generating Poisson-based spikes.

assistant: text: Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Fair Comparison of Graph Neural Networks for Graph Classification

Paper content:
Experimental reproducibility and replicability are critical topics in machine learning.
Authors have often raised concerns about their lack in scientific publications to improve the quality of the field.
Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.
As such, several Graph Neural Network models have been developed to effectively tackle graph classification.
However, experimental procedures often lack rigorousness and are hardly reproducible.
Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art.
To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks.
Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet.
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

assistant: text: We provide a rigorous comparison of different Graph Neural Networks for graph classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Siamese Capsule Networks

Paper content:
Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB.
Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable.
Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points.
In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks.
The model is trained using contrastive loss with l2-normalized capsule encoded pose features.
We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

assistant: text: A variant of capsule networks that can be used for pairwise learning tasks. Results shows that Siamese Capsule Networks work well in the few shot learning setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sub-policy Adaptation for Hierarchical Reinforcement Learning

Paper content:
Hierarchical Reinforcement Learning is a promising approach to long-horizon decision-making problems with sparse rewards.
Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task.
Treating the skills as fixed can lead to significant sub-optimality in the transfer setting.
In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task.
Our main contributions are two-fold.
First, we derive a new hierarchical policy gradient, as well as an unbiased latent-dependent baseline.
We introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy simultaneously.
Second, we propose a method of training time-abstractions that improves the robustness of the obtained skills to environment changes.
Code and results are available at sites.google.com/view/hippo-rl.

assistant: text: We propose HiPPO, a stable Hierarchical Reinforcement Learning algorithm that can train several levels of the hierarchy simultaneously, giving good performance both in skill discovery and adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Paper content:
Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions.
While architectural advances have led to improved accuracy, building robust models remains challenging, involving major changes in training procedure and datasets.
Prior work has argued that there is an inherent trade-off between robustness and accuracy, as exemplified by standard data augmentation techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy.
We introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image.
Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNet Common Corruptions benchmarks while also maintaining accuracy on clean data.
We find that this augmentation leads to reduced sensitivity to high frequency noise (similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout).
We show it can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment.
Finally, we find that the idea of restricting perturbations to patches can also be useful in the context of adversarial learning, yielding models without the loss in accuracy that is found with unconstrained adversarial training.

assistant: text: Simple augmentation method overcomes robustness/accuracy trade-off observed in literature and opens questions about the effect of training distribution on out-of-distribution generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Natural- to formal-language generation using Tensor Product Representations

Paper content:
Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output.
Most state-of-the-art neural sequence models do not explicitly capture such structure information, and thus do not perform well on these tasks.
In this paper, we propose a new encoder-decoder model based on Tensor Product Representations (TPRs) for Natural- to Formal-language generation, called TP-N2F.
The encoder of TP-N2F employs TPR 'binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR 'unbinding' to generate a sequence of relational tuples, each consisting of a relation (or operation) and a number of arguments, in symbolic space.
TP-N2F considerably outperforms LSTM-based Seq2Seq models, creating a new state of the art results on two benchmarks: the MathQA dataset for math problem solving, and the AlgoList dataset for program synthesis.
Ablation studies show that improvements are mainly attributed to the use of TPRs in both the encoder and decoder to explicitly capture relational structure information for symbolic reasoning.

assistant: text: In this paper, we propose a new encoder-decoder model based on Tensor Product Representations for Natural- to Formal-language generation, called TP-N2F.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer

Paper content:
We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure.
We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches, our model achieves higher accuracy in half as many epochs with five times fewer learnable parameters.
Our model directly models underlying question logic using a recurrent controller that jointly predicts and executes functional neural modules; it explicitly forks subprocesses to handle logical branching.
While FiLM and other competitive models are static architectures with less supervision, we argue that inclusion of program labels enables learning of higher level logical operations -- our architecture achieves particularly high performance on questions requiring counting and integer comparison. We further demonstrate the generality of our approach though DDRstack -- an application of our method to reverse Polish notation expression evaluation in which the inclusion of a stack assumption allows our approach to generalize to long expressions, significantly outperforming an LSTM with ten times as many learnable parameters.

assistant: text: A generic dynamic architecture that employs a problem specific differentiable forking mechanism to encode hard data structure assumptions. Applied to CLEVR VQA and expression evaluation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Toward Understanding the Impact of Staleness in Distributed Machine Learning

Paper content:
Most distributed machine learning (ML) systems store a copy of the model parameters locally on each machine to minimize network communication.
In practice, in order to reduce synchronization waiting time, these copies of the model are not necessarily updated in lock-step, and can become stale.
Despite much development in large-scale ML, the effect of staleness on the learning efficiency is inconclusive, mainly because it is challenging to control or monitor the staleness in complex distributed environments.
In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates.
Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature.
The empirical findings also inspire a new convergence analysis of SGD in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\sqrt{T}).

assistant: text: Empirical and theoretical study of the effects of staleness in non-synchronous execution on machine learning algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN

Paper content:
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.
Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors.
Several attempts have been made to complete knowledge graph as well as to detect noise.
However, none of them considers unifying these two tasks even though they are inter-dependent and can mutually boost the performance of each other.
In this paper, we proposed to jointly combine these two tasks with a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding.
Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms both in regard to knowledge graph completion and error detection.

assistant: text: We proposed a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BERT Wears GloVes: Distilling Static Embeddings from Pretrained Contextual Representations

Paper content:
Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks.
In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe.
However, static embeddings do have their advantages in that they are straightforward to understand and faster to use.
Additionally, embedding analysis methods for static embeddings are far more diverse and mature than those available for their dynamic counterparts.
In this work, we introduce simple methods for generating static lookup table embeddings from existing pretrained contextual representations and demonstrate they outperform Word2Vec and GloVe embeddings on a variety of word similarity and word relatedness tasks.
In doing so, our results also reveal insights that may be useful for subsequent downstream tasks using our embeddings or the original contextual models.
Further, we demonstrate the increased potential for analysis by applying existing approaches for estimating social bias in word embeddings.
Our analysis constitutes the most comprehensive study of social bias in contextual word representations (via the proxy of our distilled embeddings) and reveals a number of inconsistencies in current techniques for quantifying social bias in word embeddings.
We publicly release our code and distilled word embeddings to support reproducible research and the broader NLP community.

assistant: text: A procedure for distilling contextual models into static embeddings; we apply our method to 9 popular models and demonstrate clear gains in representation quality wrt Word2Vec/GloVe and improved analysis potential by thoroughly studying social bias.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning How to Ground a Plan - Partial Grounding in Classical Planning

Paper content:
Current classical planners are very successful in finding (non-optimal) plans, even for large planning instances.
To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task.
Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner.
To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible.
We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods.
Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.

assistant: text: This paper introduces partial grounding to tackle the problem that arises when the full grounding process, i.e., the translation of a PDDL input task into a ground representation like STRIPS, is infeasible due to memory or time constraints.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sliced Wasserstein Auto-Encoders

Paper content:
In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder.
We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified.
In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution.
We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation.
We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.

assistant: text: In this paper we use the sliced-Wasserstein distance to shape the latent distribution of an auto-encoder into any samplable prior distribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stable Opponent Shaping in Differentiable Games

Paper content:
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel – from GANs and intrinsic curiosity to multi-agent RL.
Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others’ updates.
Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner’s Dilemma.
Although experimentally successful, we show that LOLA agents can exhibit ‘arrogant’ behaviour directly at odds with convergence.
In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games.
In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead.
We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games.
SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.

assistant: text: Opponent shaping is a powerful approach to multi-agent learning but can prevent convergence; our SOS algorithm fixes this with strong guarantees in all differentiable games.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diverse Trajectory Forecasting with Determinantal Point Processes

Paper content:
The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles).
In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions.
It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode).
While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data.
In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories.
The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples.
Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation.
To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).
Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories.
Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space.
We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.

assistant: text: We learn a diversity sampling function with DPPs to obtain a diverse set of samples from a generative model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Context-Aware Learning to Rank with Self-Attention

Paper content:
In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users.
Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss.
The list is then sorted in the descending order of the scores.
Possible interactions between items present in the same list are taken into account in the training phase at the loss level.
However, during inference, items are scored individually, and possible interactions between them are not considered.
In this paper, we propose a context-aware neural network model that learns item scores by applying a self-attention mechanism.
The relevance of a given item is thus determined in the context of all other items present in the list, both in training and in inference.
Finally, we empirically demonstrate significant performance gains of self-attention based neural architecture over Multi-Layer Perceptron baselines.
This effect is consistent across popular pointwise, pairwise and listwise losses on datasets with both implicit and explicit relevance feedback.

assistant: text: Learning to rank using the Transformer architecture.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Domain Adaptation By Augmented Cyclic Adversarial Learning

Paper content:
Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.
However, it is often the case that data are abundant in some domains but scarce in others.
Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain.
In general, this requires learning plausible mappings between domains.
CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint.
However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data.
In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction.
This task specific model both relaxes the cycle-consistency constraint and complements the role of the discriminator during training, serving as an augmented information source for learning the mapping.
We explore adaptation in speech and visual domains in low resource in supervised setting.
In speech domains, we adopt a speech recognition model from each domain as the task specific model.
Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices.
In low-resource visual domain adaptation, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain.

assistant: text: A robust domain adaptation by employing a task specific loss in cyclic adversarial learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ProMP: Proximal Meta-Policy Search

Paper content:
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively.
This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies.
This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL.
Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients.
By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning.
Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.

assistant: text: A novel and theoretically grounded meta-reinforcement learning algorithm
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning

Paper content:
The purpose of an encoding model is to predict brain activity given a stimulus.
In this contribution, we attempt at estimating a whole brain encoding model of auditory perception in a naturalistic stimulation setting.
We analyze data from an open dataset, in which 16 subjects watched a short movie while their brain activity was being measured using functional MRI.
We extracted feature vectors aligned with the timing of the audio from the movie, at different layers of a Deep Neural Network pretrained on the classification of auditory scenes.
fMRI data was parcellated using hierarchical clustering on 500 parcels, and encoding models were estimated using a fully connected neural network with one hidden layer, trained to predict the signals for each parcel from the DNN features.
Individual encoding models were successfully trained and predicted brain activity on unseen data, in parcels located in the superior temporal lobe, as well as dorsolateral prefrontal regions, which are usually considered as areas involved in auditory and language processing.
Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings.

assistant: text: Feature vectors from SoundNet can predict brain activity of subjects watching a movie in auditory and language related brain regions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ensemble Adversarial Training: Attacks and Defenses

Paper content:
Adversarial examples are perturbed inputs designed to fool machine learning models.
Adversarial training injects such examples into training data to increase robustness.
To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss.
We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.
The model thus learns to generate weak perturbations, rather than defend against strong ones.
As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step.
We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models.
On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks.
In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks.

assistant: text: Adversarial training with single-step methods overfits, and remains vulnerable to simple black-box and white-box attacks. We show that including adversarial examples from multiple sources helps defend against black-box attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hyperparameter Tuning and Implicit Regularization in Minibatch SGD

Paper content:
This paper makes two contributions towards understanding how the hyperparameters of stochastic gradient descent affect the final training loss and test accuracy of neural networks.
First, we argue that stochastic gradient descent exhibits two regimes with different behaviours; a noise dominated regime which typically arises for small or moderate batch sizes, and a curvature dominated regime which typically arises when the batch size is large.
In the noise dominated regime, the optimal learning rate increases as the batch size rises, and the training loss and test accuracy are independent of batch size under a constant epoch budget.
In the curvature dominated regime, the optimal learning rate is independent of batch size, and the training loss and test accuracy degrade as the batch size rises.
We support these claims with experiments on a range of architectures including ResNets, LSTMs and autoencoders.
We always perform a grid search over learning rates at all batch sizes.
Second, we demonstrate that small or moderately large batch sizes continue to outperform very large batches on the test set, even when both models are trained for the same number of steps and reach similar training losses.
Furthermore, when training Wide-ResNets on CIFAR-10 with a constant batch size of 64, the optimal learning rate to maximize the test accuracy only decays by a factor of 2 when the epoch budget is increased by a factor of 128, while the optimal learning rate to minimize the training loss decays by a factor of 16.
These results confirm that the noise in stochastic gradients can introduce beneficial implicit regularization.

assistant: text: Smaller batch sizes can outperform very large batches on the test set under constant step budgets and with properly tuned learning rate schedules.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Variational Learning of Controllable Representations for Text without Supervision

Paper content:
The variational autoencoder (VAE) has found success in modelling the manifold of natural images on certain datasets, allowing meaningful images to be generated while interpolating or extrapolating in the latent code space, but it is unclear whether similar capabilities are feasible for text considering its discrete nature.
In this work, we investigate the reason why unsupervised learning of controllable representations fails for text.
We find that traditional sequence VAEs can learn disentangled representations through their latent codes to some extent, but they often fail to properly decode when the latent factor is being manipulated, because the manipulated codes often land in holes or vacant regions in the aggregated posterior latent space, which the decoding network is not trained to process.
Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex.
Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text.
Empirically, our method significantly outperforms unsupervised baselines and is competitive with strong supervised approaches on text style transfer.
Furthermore, when switching the latent factor (e.g., topic) during a long sentence generation, our proposed framework can often complete the sentence in a seemingly natural way -- a capability that has never been attempted by previous methods.

assistant: text: why previous VAEs on text cannot learn controllable latent representation as on images, as well as a fix to enable the first success towards controlled text generation without supervision
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Statistical Approach to Assessing Neural Network Robustness

Paper content:
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated.
Specifically, we estimate the probability of the event that the property is violated under an input model.
Our approach critically varies from the formal verification framework in that when the property can be violated, it provides an informative notion of how robust the network is, rather than just the conventional assertion that the network is not verifiable.
Furthermore, it provides an ability to scale to larger networks than formal verification approaches.
Though the framework still provides a formal guarantee of satisfiability whenever it successfully finds one or more violations, these advantages do come at the cost of only providing a statistical estimate of unsatisfiability whenever no violation is found.
Key to the practical success of our approach is an adaptation of multi-level splitting, a Monte Carlo approach for estimating the probability of rare events, to our statistical robustness framework.
We demonstrate that our approach is able to emulate formal verification procedures on benchmark problems, while scaling to larger networks and providing reliable additional information in the form of accurate estimates of the violation probability.

assistant: text: We introduce a statistical approach to assessing neural network robustness that provides an informative notion of how robust a network is, rather than just the conventional binary assertion of whether or not of property is violated.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Parameter Space Noise for Exploration

Paper content:
Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space.
An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors.
Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples.
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks.

assistant: text: Parameter space noise allows reinforcement learning algorithms to explore by perturbing parameters instead of actions, often leading to significantly improved exploration performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

Paper content:
When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas.
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
We present a unified framework, based on the relation-aware self-attention mechanism,to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.
On the challenging Spider dataset this framework boosts the exact match accuracy to 53.7%, compared to 47.4% for the previous state-of-the-art model unaugmented with BERT embeddings.
In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment.

assistant: text: State of the art in complex text-to-SQL parsing by combining hard and soft relational reasoning in schema/question encoding.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Paper content:
Semi-supervised learning, i.e. jointly learning from labeled an unlabeled samples, is an active research topic due to its key role on relaxing human annotation constraints.
In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different  perturbations of unlabeled samples.
We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions.
We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it.
The proposed approach achieves state-of-the-art results in CIFAR-10/100 and Mini-ImageNet despite being much simpler than other state-of-the-art.
These results demonstrate that pseudo-labeling can outperform consistency regularization methods, while the opposite was supposed in previous work.
Code will be made available.

assistant: text: Pseudo-labeling has shown to be a weak alternative for semi-supervised learning. We, conversely, demonstrate that dealing with confirmation bias with several regularizations makes pseudo-labeling a suitable approach.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning

Paper content:
Deep Neural Networks (DNNs) are known for excellent performance in supervised tasks such as classification.
Convolutional Neural Networks (CNNs), in particular, can learn effective features and build high-level representations that can be used for
classification, but also for querying and nearest neighbor search.
However, CNNs have also been shown to suffer from a performance drop when the distribution of the data changes from training to test data.
In this paper we analyze the internal
representations of CNNs and observe that the representations of unseen data in each class, spread more (with higher variance) in the embedding space of the CNN compared to representations of the training data.
More importantly, this difference is more extreme if the unseen data comes from a shifted distribution.
Based on this observation, we objectively evaluate the degree of representation’s variance in each class by applying eigenvalue decomposition on the within-class covariance of the internal representations of CNNs and observe the same behaviour.
This can be problematic as larger variances might lead to mis-classification if the sample crosses the decision boundary of its class.
We apply nearest neighbor classification on the representations and empirically show that the embeddings with the high variance actually have significantly worse KNN classification performances, although this could not be foreseen from their end-to-end classification results.
To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN’s representation, improving performance on unseen test data from a shifted distribution.
We empirically evaluate DWCCA on two datasets for Acoustic Scene Classification (DCASE2016 and DCASE2017).
We demonstrate that not only does DWCCA significantly improve the network’s internal representation, it
also increases the end-to-end classification accuracy, especially when the test set exhibits a slight distribution shift.
By adding DWCCA to a VGG neural network, we achieve around 6 percentage points improvement in the case of a distribution
mismatch.

assistant: text: We propose a novel deep neural network layer for normalising within-class covariance of an internal representation in a neural network that results in significantly improving the generalisation of the learned representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LOGAN:  Latent Optimisation for Generative Adversarial Networks

Paper content:
Training generative adversarial networks requires balancing of delicate adversarial dynamics.
Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes.
In this work, we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator.
We develop supporting theoretical analysis from the perspectives of differentiable games and stochastic approximation.
Our experiments demonstrate that latent optimisation can significantly improve GAN training, obtaining state-of-the-art performance for the ImageNet (128 x 128) dataset.
Our model achieves an Inception Score (IS) of 148 and an Frechet Inception Distance (FID) of 3.4, an improvement of 17% and 32% in IS and FID respectively, compared with the baseline BigGAN-deep model with the same architecture and number of parameters.

assistant: text: Latent optimisation improves adversarial training dynamics. We present both theoretical analysis and state-of-the-art image generation with ImageNet 128x128.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: State Space LSTM Models with Particle MCMC Inference

Paper content:
Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Despite the strong performance, however, it lacks the nice interpretability as in state space models.
In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \cite{zaheer2017latent} of combining topic models with LSTM.
However, unlike \cite{zaheer2017latent}, we do not make any factorization assumptions in our inference algorithm.
We present an efficient sampler based on sequential Monte Carlo (SMC) method that draws from the joint posterior directly.
Experimental results confirms the superiority and stability of this SMC inference algorithm on a variety of domains.

assistant: text: We present State Space LSTM models, a combination of state space models and LSTMs, and propose an inference algorithm based on sequential Monte Carlo.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback

Paper content:
We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode.
We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function.
RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation.
RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings.

assistant: text: We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms

Paper content:
Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science.
This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms.
After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size.
We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances).
Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah.
We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.

assistant: text: This paper compares statistical tests for RL comparisons (false positive, statistical power), checks robustness to assumptions using simulated distributions and empirical distributions (SAC, TD3), provides guidelines for RL students and researchers.