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

Title: One Generation Knowledge Distillation by Utilizing Peer Samples

Paper content:
Knowledge Distillation (KD) is a widely used technique in recent deep learning research to obtain small and simple models whose performance is on a par with their large and complex counterparts.
Standard Knowledge Distillation tends to be time-consuming because of the training time spent to obtain a teacher model that would then provide guidance for the student model.
It might be possible to cut short the time by training a teacher model on the fly, but it is not trivial to have such a high-capacity teacher that gives quality guidance to student models this way.
To improve this, we present a novel framework of Knowledge Distillation exploiting dark knowledge from the whole training set.
In this framework, we propose a simple and effective implementation named Distillation by Utilizing Peer Samples (DUPS) in one generation.
We verify our algorithm on numerous experiments.
Compared with standard training on modern architectures, DUPS achieves an average improvement of 1%-2% on various tasks with nearly zero extra cost.
Considering some typical Knowledge Distillation methods which are much more time-consuming, we also get comparable or even better performance using DUPS.

assistant: text: We present a novel framework of Knowledge Distillation utilizing peer samples as the teacher
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints

Paper content:
The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.
While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.
We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.
We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.
Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.
Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.
%The resulting model encodes relations that better generalize across instances.
In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.
We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.
It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.

assistant: text: The authors propose a framework to incorporate additional semantic prior knowledge into the traditional training of deep learning models to regularize the embedding space instead of the parameter space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Markov Logic Networks

Paper content:
We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.
Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules.
Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure.
Interestingly, any MLN can be represented as an NMLN.
Similarly to recently proposed Neural theorem provers (NTPs) (Rocktaschel at al. 2017), NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence.
This is extremely important for predicting in settings other than the transductive one.
We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.

assistant: text: The paper provides an extension to Markov Logic Networks by removing their dependency on pre-defined first-order logic rules to model more domains in knowledge-base completion tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference

Paper content:
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).
Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier.
In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.
Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?
This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a “sweet point" in co-optimizing model accuracy, robustness, and efficiency.
Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction.
That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.
We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.

assistant: text: Exploits input-adaptive multiple early-exits for the field of adversarial attack and defense, reducing the average inference complexity without conflicting the larger capacity assumption.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MultiGrain: a unified image embedding for classes and instances

Paper content:
We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition.
MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss.
The self-supervised loss only uses data augmentation and thus does not require additional labels.
Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy.
When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method.
The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution.
An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.

assistant: text: The paper proposes to jointy train a deep neural net for image classification, instance, and copy recognition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Contrastive Representation Distillation

Paper content:
Often we wish to transfer representational knowledge from one neural network to another.
Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator.
Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network.
We demonstrate that this objective ignores important structural knowledge of the teacher network.
This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data.
We formulate this objective as contrastive learning.
Experiments demonstrate that our resulting new objective outperforms knowledge distillation on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer.
When combined with knowledge distillation, our method sets a state of the art in many transfer tasks, sometimes even outperforming the teacher network.

assistant: text: Representation/knowledge distillation by maximizing mutual information between teacher and student
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models

Paper content:
In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator.
We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task.
We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training.
We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer.
Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named "mix-review''.
We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated.
Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

assistant: text: We identify the forgetting problem in fine-tuning of pre-trained NLG models, and propose the mix-review strategy to address it.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Paper content:
We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge.
In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions.
We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables).
We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables).
We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs.
Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning.
Experiments in the two-variable case validate the proposed ideas and theoretical results.

assistant: text: This paper proposes a meta-learning objective based on speed of adaptation to transfer distributions to discover a modular decomposition and causal variables.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SoftLoc: Robust Temporal Localization under Label Misalignment

Paper content:
This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data.
We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning.
Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead.
Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences.
We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.

assistant: text: This work introduces a novel loss function for the robust training of temporal localization DNN in the presence of misaligned labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Simultaneous Classification and Out-of-Distribution Detection Using Deep Neural Networks

Paper content:
Deep neural networks have achieved great success in classiﬁcation tasks during the last years.
However, one major problem to the path towards artiﬁcial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classiﬁcation algorithms assume that all classes are known prior to the training stage.
In this work, we propose a methodology for training a neural network that allows it to efﬁciently detect out-of-distribution (OOD) examples without compromising much of its classiﬁcation accuracy on the test examples from known classes.
Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classiﬁcation tasks.
Additionally, the way this method was constructed makes it suitable for training any classiﬁcation algorithm that is based on Maximum Likelihood methods.

assistant: text: We propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with Outlier Exposure both on image and text classiﬁcation tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Relevant-features based Auxiliary Cells for Robust and Energy Efficient Deep Learning

Paper content:
Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks.
However, they have no inherent capability to recognize when their predictions might go wrong.
There have been several efforts in the recent past to detect natural errors i.e.  misclassified inputs but these mechanisms pose additional energy requirements.
To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way.
We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs).
The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs.
The combined confidence of RACs is utilized to determine if classification should terminate at an early stage.
We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet.
Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.

assistant: text: Improve the robustness and energy efficiency of a deep neural network using the hidden representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Preliminary theoretical troubleshooting in Variational Autoencoder

Paper content:
What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE?
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension.
Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension.
On disentanglement issue,   subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made.
On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class.
On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors.
On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.

assistant: text: Modifying how noise factors are treated when developing VAE models
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FAST LEARNING VIA EPISODIC MEMORY: A PERSPECTIVE FROM ANIMAL DECISION-MAKING

Paper content:
A typical experiment to study cognitive function is to train animals to perform tasks, while the researcher records the electrical activity of the animals neurons.
The main obstacle faced, when using this type of electrophysiological experiment to uncover the circuit mechanisms underlying complex behaviors, is our incomplete access to relevant circuits in the brain.
One promising approach is to model neural circuits using an artificial neural network (ANN), which can provide complete access to the “neural circuits” responsible for a behavior.
More recently, reinforcement learning models have been adopted to understand the functions of cortico-basal ganglia circuits as reward-based learning has been found in mammalian brain.
In this paper, we propose a Biologically-plausible Actor-Critic with Episodic Memory (B-ACEM) framework to model a prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit, which is verified to capture the behavioral findings from a well-known perceptual decision-making task, i.e., random dots motion discrimination.
This B-ACEM framework links neural computation to behaviors, on which we can explore how episodic memory should be considered to govern future decision.
Experiments are conducted using different settings of the episodic memory and results show that all patterns of episodic memories can speed up learning.
In particular, salient events are prioritized to propagate reward information and guide decisions.
Our B-ACEM framework and the built-on experiments give inspirations to both designs for more standard decision-making models in biological system and a more biologically-plausible ANN.

assistant: text: Fast learning via episodic memory verified by a biologically plausible framework for prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CATS: Customizable Abstractive Topic-based Summarization

Paper content:
Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text.
Despite the advances in abstractive summarization, custom generation of summaries (e.g. towards a user's preference) remains unexplored.
In this paper, we present CATS, an abstractive neural summarization model, that summarizes content in a sequence-to-sequence fashion but also introduces a new mechanism to control the underlying latent topic distribution of the produced summaries.
Our experimental results on the well-known CNN/DailyMail dataset show that our model achieves state-of-the-art performance.

assistant: text: We present the first neural abstractive summarization model capable of customization of generated summaries.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Paper content:
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.
However, learning from demonstrations often suffers from the covariate shift problem, which results
in cascading errors of the learned policy.
We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction.
We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation.
We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks.
We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.

assistant: text: We introduce a notion of conservatively-extrapolated value functions, which provably lead to policies that can self-correct to stay close to the demonstration states, and learn them with a novel negative sampling technique.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Online abstraction with MDP homomorphisms for Deep Learning

Paper content:
Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy.
In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces.
It is based on MDP homomorphisms, a structure-preserving mapping between MDPs.
We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters.
Our novel task transfer method beats a baseline based on a deep Q-network.

assistant: text: We create abstract models of environments from experience and use them to learn new tasks faster.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks

Paper content:
The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks.
However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence.
To address this challenge, we introduce a general end-to-end graph-to-sequence neural encoder-decoder architecture that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.
Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.
We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs.
Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.

assistant: text: This work proposes an end-to-end graph encoder to sequence decoder models with an attention mechanism in between.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universality Theorems for Generative Models

Paper content:
Despite the fact that generative models are extremely successful in practice, the theory underlying this phenomenon is only starting to catch up with practice.
In this work we address the question of the universality of generative models: is it true that neural networks can approximate any data manifold arbitrarily well?
We provide a positive answer to this question and show that under mild assumptions on the activation function one can always find a feedforward neural network that maps the latent space onto a set located within the specified Hausdorff distance from the desired data manifold.
We also prove similar theorems for the case of multiclass generative models and cycle generative models, trained to map samples from one manifold to another and vice versa.

assistant: text: We shot that a wide class of manifolds can be generated by ReLU and sigmoid networks with arbitrary precision.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Relevant-features based Auxiliary Cells for Robust and Energy Efficient Deep Learning

Paper content:
Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks.
However, they have no inherent capability to recognize when their predictions might go wrong.
There have been several efforts in the recent past to detect natural errors i.e.  misclassified inputs but these mechanisms pose additional energy requirements.
To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way.
We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs).
The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs.
The combined confidence of RACs is utilized to determine if classification should terminate at an early stage.
We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet.
Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.

assistant: text: This paper aims to reduce the misclassifications of deep neural networks in an energy efficient way by adding Relevant feature based Auxiliary Cells after one or more hidden layers to decide whether to end classification early.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AdamT: A Stochastic Optimization with Trend Correction Scheme

Paper content:
Adam-typed optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning.
Such methods are appealing for capability on large-scale sparse datasets.
On top of that, they are computationally efficient and insensitive to the hyper-parameter settings.
In this paper, we present a new framework for adapting Adam-typed methods, namely AdamT.
Instead of applying a simple exponential weighted average, AdamT also includes the trend information when updating the parameters with the adaptive step size and gradients.
The newly added term is expected to efficiently capture the non-horizontal moving patterns on the cost surface, and thus converge more rapidly.
We show empirically the importance of the trend component, where AdamT outperforms the conventional Adam method constantly in both convex and non-convex settings.

assistant: text: A new type of Adam variant that uses Holt's linear method to compute the smoothed first order and second order momentum instead of using exponential weighted average.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Paper content:
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment.
Computing a Bayes-optimal policy is however intractable for all but the smallest tasks.
In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection.
In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty.
We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.

assistant: text: VariBAD opens a path to tractable approximate Bayes-optimal exploration for deep RL using ideas from meta-learning, Bayesian RL, and approximate variational inference.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: When Does Self-supervision Improve Few-shot Learning?

Paper content:
We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions.
Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown.
We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets.
The improvements are greater when the training set is smaller or the task is more challenging.
Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL.
Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements.
We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.

assistant: text: An empirical study of different self-supervised learning (SSL) methods, showing SSL helps more when the dataset is harder, that domain matters for training, and a method to choose samples from an unlabeled dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Retrieving Signals in the Frequency Domain with Deep Complex Extractors

Paper content:
Recent advances have made it possible to create deep complex-valued neural networks.
Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems.
Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain.
As a case study, we perform audio source separation in the Fourier domain.
Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation.
We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters.
Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect.

assistant: text: New Signal Extraction Method in the Fourier Domain
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Paper content:
Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling 2013, Rezende et al. 2014).
These approaches maximize a variational lower bound on the intractable log likelihood of the observed data.
Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases.
Counterintuitively, the typical inference network gradient estimator for the IWAE bound performs poorly as the number of samples increases (Rainforth et al. 2018, Le et al. 2018).
Roeder et a.
(2017) propose an improved gradient estimator, however, are unable to show it is unbiased.
We show that it is in fact biased and that the bias can be estimated efficiently with a second application of the reparameterization trick.
The doubly reparameterized gradient (DReG) estimator does not suffer as the number of samples increases, resolving the previously raised issues.
The same idea can be used to improve many recently introduced training techniques for latent variable models.
In particular, we show that this estimator reduces the variance of the IWAE gradient, the reweighted wake-sleep update (RWS) (Bornschein & Bengio 2014), and the jackknife variational inference (JVI) gradient (Nowozin 2018).
Finally, we show that this computationally efficient, drop-in estimator translates to improved performance for all three objectives on several modeling tasks.

assistant: text: Author experimentally found that the estimator of the existing work(STL) is biased and proposes to reduce the bias to improve the gradient estimator of the ELBO.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lifelong Word Embedding via Meta-Learning

Paper content:
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks.
On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks.
On the other hand, many domain-specific tasks do not have a large enough domain corpus to obtain high-quality embeddings.
We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.
In this paper, we formulate the learning of word embeddings as a lifelong learning process.
Given knowledge learned from many previous domains and a small new domain corpus, the proposed method can effectively generate new domain embeddings by leveraging a simple but effective algorithm and a meta-learner, where the meta-learner is able to provide word context similarity information at the domain-level.
Experimental results demonstrate that the proposed method can effectively learn new domain embeddings from a small corpus and past domain knowledges\footnote{We will release the code after final revisions.}.
We
also demonstrate that general-purpose embeddings trained from a large scale corpus are sub-optimal in domain-specific tasks.

assistant: text: This paper proposes an approach to learn embeddings in new domains and significantly beats the baseline on an aspect extraction task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Point Cloud GAN

Paper content:
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data.
In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.
We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN).
First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process.
A key component of our method is that we train a posterior inference network for the hidden variables.
Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms.
We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN.
We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods.
We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.

assistant: text: We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate,  conditional generation, generalization to unseen point clouds and image to point cloud.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Challenges in Disentangling Independent Factors of Variation

Paper content:
We study the problem of building models that disentangle independent factors of variation.
Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.
As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.
This labeling is of particular interest as it may be readily available without annotation costs.
We introduce an autoencoder model and train it through constraints on image pairs and triplets.
We show the role of feature dimensionality and adversarial training theoretically and experimentally.
We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used.
The numerical value of a factor has different meaning in different reference frames.
When the reference depends on other factors, transferring that factor becomes ambiguous.
We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.

assistant: text: This paper studies the challenges of disentangling independent factors of variation under weakly labeled data and introduces the term reference ambiguity for data point mapping.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

Paper content:
An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning.
An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks.
Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse,  with the meta initialization already containing high quality features?
We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor.
This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of the underlying neural network.
ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML.
We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm).
We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

assistant: text: The paper finds that feature reuse is the dominant factor in the success of MAML, and propose new algorithms which spend much less computation than MAML.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts

Paper content:
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people.
We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs).
While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces.
We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve.
We split the challenge into two simpler components.
First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy.
Second, we train a Bayesian residual policy to improve upon the ensemble's recommendation and learn to reduce uncertainty.
Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models.
BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.

assistant: text: This paper considers Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs) by making decisions with experts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Perturbation Analysis of Input Transformations for Adversarial Attacks

Paper content:
The existence of adversarial examples, or intentional mis-predictions constructed from small changes to correctly predicted examples, is one of the most significant challenges in neural network research today.
Ironically, many new defenses are based on a simple observation - the adversarial inputs themselves are not robust and small perturbations to the attacking input often recover the desired prediction.
While the intuition is somewhat clear, a detailed understanding of this phenomenon is missing from the research literature.
This paper presents a comprehensive experimental analysis of when and why perturbation defenses work and potential mechanisms that could explain their effectiveness (or ineffectiveness) in different settings.

assistant: text: We identify a family of defense techniques and show that both deterministic lossy compression and randomized perturbations to the input lead to similar gains in robustness.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How transferable are features in convolutional neural network acoustic models across languages?

Paper content:
Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies.
Here we study convolutional neural network-based acoustic models in the context of automatic speech recognition.
Adapting a method proposed by Yosinski et al. [2014], we measure the transferability of each layer between German and English to assess the their language-specifity.
We observe three distinct regions of transferability: (1) the first two layers are entirely transferable between languages, (2) layers 2–8 are also highly transferable but we find evidence of some language specificity, (3) the subsequent fully connected layers are more language specific but can be successfully finetuned to the target language.
To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [Raghu et al., 2017].
Our results are consistent with the observation that CCNs converge 'bottom up' during training and demonstrate the benefit of freeze training, especially for transfer learning.

assistant: text: All but the first two layers of our CNNs based acoustic models demonstrated some degree of language-specificity but freeze training enabled successful transfer between languages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving the robustness of ImageNet classifiers using elements of human visual cognition

Paper content:
We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale.
As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models.
It does not, however, improve the robustness against more natural, and typically larger, perturbations.
Learning more robust features during training appears to be necessary for robustness in this second sense.
We show that features derived from a model that was encouraged to learn global, shape-based representations (Geirhos et al., 2019) do not only improve the robustness against natural perturbations, but when used in conjunction with an episodic memory, they also provide additional robustness against adversarial perturbations.
Finally, we address three important design choices for the episodic memory: memory size, dimensionality of the memories and the retrieval method.
We show that to make the episodic memory more compact, it is preferable to reduce the number of memories by clustering them, instead of reducing their dimensionality.

assistant: text: This paper proposed to use memory cache to improve robustness against adversarial image examples, and concluded that using a large continous cache is not superior to hard attention.