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

Title: PLEX: PLanner and EXecutor for Embodied Learning in Navigation

Paper content:
We present a method for policy learning to navigate indoor environments.
We adopt a hierarchical policy approach, where two agents are trained to work in cohesion with one another to perform a complex navigation task.
A Planner agent operates at a higher level and proposes sub-goals for an Executor agent.
The Executor reports an embedding summary back to the Planner as additional side information at the end of its series of operations for the Planner's next sub-goal proposal.
The end goal is generated by the environment and exposed to the Planner which then decides which set of sub-goals to propose to the Executor.
We show that this Planner-Executor setup drastically increases the sample efficiency of our method over traditional single agent approaches, effectively mitigating the difficulty accompanying long series of actions with a sparse reward signal.
On the challenging Habitat environment which requires navigating various realistic indoor environments, we demonstrate that our approach offers a significant improvement over prior work for navigation.

assistant: text: We present a hierarchical learning framework for navigation within an embodied learning setting
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks

Paper content:
The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance.
Yet despite significant empirical and theoretical analysis, relatively little has been proved about the concrete effects of different initialization schemes.
In this work, we analyze the effect of initialization in deep linear networks, and provide for the first time a rigorous proof that drawing the initial weights from the orthogonal group speeds up convergence relative to the standard Gaussian initialization with iid weights.
We show that for deep networks, the width needed for efficient convergence to a global minimum with orthogonal initializations is independent of the depth, whereas the width needed for efficient convergence with Gaussian initializations scales linearly in the depth.
Our results demonstrate how the benefits of a good initialization can persist throughout learning, suggesting an explanation for the recent empirical successes found by initializing very deep non-linear networks according to the principle of dynamical isometry.

assistant: text: We provide for the first time a rigorous proof that orthogonal initialization speeds up convergence relative to Gaussian initialization, for deep linear networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A cluster-to-cluster framework for neural machine translation

Paper content:
The quality of a machine translation system depends largely on the availability of sizable parallel corpora.
For the recently popular Neural Machine Translation (NMT) framework, data sparsity problem can become even more severe.
With large amount of tunable parameters, the NMT model may overfit to the existing language pairs while failing to understand the general diversity in language.
In this paper, we advocate to broadcast every sentence pair as two groups of similar sentences to incorporate more diversity in language expressions, which we name as parallel cluster.
Then we define a more general cluster-to-cluster correspondence score and train our model to maximize this score.
Since direct maximization is difficult, we derive its lower-bound as our surrogate objective, which is found to generalize point-point Maximum Likelihood Estimation (MLE) and point-to-cluster Reward Augmented Maximum Likelihood (RAML) algorithms as special cases.
Based on this novel objective function, we delineate four potential systems to realize our cluster-to-cluster framework and test their performances in three recognized translation tasks, each task with forward and reverse translation directions.
In each of the six experiments, our proposed four parallel systems have consistently proved to outperform the MLE baseline, RL (Reinforcement Learning) and RAML systems significantly.
Finally, we have performed case study to empirically analyze the strength of the cluster-to-cluster NMT framework.

assistant: text: We invent a novel cluster-to-cluster framework for NMT training, which can better understand the both source and target language diversity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Strategy Synthesis in POMDPs via Game-Based Abstractions

Paper content:
Partially observable Markov decision processes (POMDPs) are a natural model for scenarios where one has to deal with incomplete knowledge and random events.
Applications include, but are not limited to, robotics and motion planning.
However, many relevant properties of POMDPs are either undecidable or very expensive to compute in terms of both runtime and memory consumption.
In our work, we develop a game-based abstraction method that is able to deliver safe bounds and tight
  approximations for important sub-classes of such properties.
We discuss the theoretical implications and showcase the applicability of our results on a broad spectrum of benchmarks.
We developed a game-based abstraction technique to synthesize strategies for a class of POMDPs.
This class encompasses typical grid-based motion planning problems under restricted observability of the environment.
For these scenarios, we efficiently compute strategies that allow the agent to maneuver the grid in order to reach a given goal state while at the same time avoiding collisions with faster moving obstacles.
Experiments show that our approach can handle state spaces up to three orders of magnitude larger than general-purpose state-of-the-art POMDP solvers in less time, while at the same time using fewer states to represent the same grid sizes.

assistant: text: This paper provides a game-based abstraction scheme to compute provably sound policies for POMDPs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network

Paper content:
In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions.
Unlike other valuable items, license plates are not allocated an estimated price before auction.
I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics.
I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate.
I demonstrate the importance of having a deep network and of retraining.
Evaluated on 13 years of historical auction prices, the deep RNN's predictions can explain over 80 percent of price variations, outperforming previous models by a significant margin.
I also demonstrate how the model can be extended to become a search engine for plates and to provide estimates of the expected price distribution.

assistant: text: Predicting auction price of vehicle license plates in Hong Kong with deep recurrent neural network, based on the characters on the plates.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RTC-VAE: HARNESSING THE PECULIARITY OF TOTAL CORRELATION  IN LEARNING DISENTANGLED REPRESENTATIONS

Paper content:
In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables.
Unfortunately, this well-motivated strategy often fail to achieve disentanglement due to a problematic difference between the sampled latent representation and its corresponding mean representation.
We provide a theoretical explanation that low total correlation of sample distribution cannot guarantee low total correlation of the  mean representation.
We prove that for the mean representation of arbitrarily high total correlation, there exist distributions of latent variables of abounded total correlation.
However, we still believe that total correlation could be a key to the disentanglement of unsupervised representative learning, and we propose a remedy,  RTC-VAE, which rectifies the total correlation penalty.
Experiments show that our model has a more reasonable distribution of the mean representation compared with baseline models, e.g.,β-TCVAE and FactorVAE.

assistant: text: diagnosed all the problem of STOA VAEs theoretically and qualitatively
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Latent Representations for Inverse Dynamics using Generalized Experiences

Paper content:
Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals.
Popular deep reinforcement learning approaches in this direction involve learning goal-conditioned policies or value functions, or Inverse Dynamics Models (IDMs).
IDMs map an agent’s current state and desired goal to the required actions.
We show that the key to achieving good performance with IDMs lies in learning the information shared between equivalent experiences, so that they can be generalized to unseen scenarios.
We design a training process that guides the learning of latent representations to encode this shared information.
Using a limited number of environment interactions, our agent is able to efficiently navigate to arbitrary points in the goal space.
We demonstrate the effectiveness of our approach in high-dimensional locomotion environments such as the Mujoco Ant, PyBullet Humanoid, and PyBullet Minitaur.
We provide quantitative and qualitative results to show that our method clearly outperforms competing baseline approaches.

assistant: text: We show that the key to achieving good performance with IDMs lies in learning latent representations to encode the information shared between equivalent experiences, so that they can be generalized to unseen scenarios.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How much Position Information Do Convolutional Neural Networks Encode?

Paper content:
In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent.
An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image.
Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so.
In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks.
A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs.

assistant: text: Our work shows positional information has been implicitly encoded in a network. This information is important for detecting position-dependent features, e.g. semantic and saliency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisiting the Generalization of Adaptive Gradient Methods

Paper content:
A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization.
We re-examine this belief both theoretically and experimentally, in light of insights and trends from recent years.
We revisit some previous oft-cited experiments and theoretical accounts in more depth, and provide a new set of experiments in larger-scale, state-of-the-art settings.
We conclude that with proper tuning, the improved training performance of adaptive optimizers does not in general carry an overfitting penalty, especially in contemporary deep learning.
Finally, we synthesize a ``user's guide'' to adaptive optimizers, including some proposed modifications to AdaGrad to mitigate some of its empirical shortcomings.

assistant: text: Adaptive gradient methods, when done right, do not incur a generalization penalty.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings

Paper content:
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task.
Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query.
However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point.
Furthermore, prior work can only handle queries that use conjunctions ($\wedge$) and existential quantifiers ($\exists$).
Handling queries with logical disjunctions ($\vee$) remains an open problem.
Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\wedge$, $\vee$, and $\exists$ operators in massive and incomplete KGs.
Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities.
However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\wedge$, $\vee$, $\exists$ in a scalable manner.
We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.

assistant: text: Answering a wide class of logical queries over knowledge graphs with box embeddings in vector space
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models

Paper content:
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.
This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues.
The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses.
An interactive demo is ready to show at the workshop.
And code will be available at github soon.

assistant: text: Print the input sentence and current response sentence onto an image and use fine-tuned ImageNet CNN model to predict the next response word.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding Composition of Word Embeddings via Tensor Decomposition

Paper content:
Word embedding is a powerful tool in natural language processing.
In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase.
We give a generative model that can capture specific syntactic relations between words.
Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition.
The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings.
We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.

assistant: text: We present a generative model for compositional word embeddings that captures syntactic relations, and provide empirical verification and evaluation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Classification-Based Perspective on GAN Distributions

Paper content:
A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on.
The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability.
In this paper, we propose new techniques that employ classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data.
These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets.
They also indicate that GANs have significant problems in reproducing the more distributional properties of the training dataset.
In particular, the diversity of such synthetic data is orders of magnitude smaller than that of the original data.

assistant: text: We propose new methods for evaluating and quantifying the quality of synthetic GAN distributions from the perspective of classification tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Latent Transformations for Object  View Points Synthesis

Paper content:
We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications.
In contrast to existing conditional
generative models which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit (CTU), designed to learn the latent space transformations corresponding to specified target views.
In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views, and an adaptive discriminator is introduced to improve the adversarial training process.
The generality of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view reconstruction on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects.
The proposed model is shown to exceed state-of-the-art results in each category while simultaneously achieving a reduction in the computational demand required for inference by 30% on average.

assistant: text: We introduce an effective, general framework for incorporating conditioning information into inference-based generative models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A theoretical framework for deep and locally connected ReLU network

Paper content:
Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success.
In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity.
The framework bridges data distribution with gradient descent rules, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm, after a novel discovery of its projection nature.
The framework is built upon teacher-student setting, by projecting the student's forward/backward pass onto the teacher's computational graph.
We do not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc).
Our framework could help facilitate theoretical analysis of many practical issues, e.g. disentangled representations in deep networks.

assistant: text: This paper presents a theoretical framework that models data distribution explicitly for deep and locally connected ReLU network
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Scalable and Efficient Computation of Large Scale Optimal Transport

Paper content:
Optimal Transport (OT) naturally arises in many machine learning applications, where we need to handle cross-modality data from multiple sources.
Yet the heavy computational burden limits its wide-spread uses.
To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport).
Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem.
We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms.
We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations.
Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior.
SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation.

assistant: text: Use GAN-based method to scalably solve optimal transport
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Loss Functions for Multiset Prediction

Paper content:
We study the problem of multiset prediction.
The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items.
Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging.
In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.
The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions.
The experiments reveal the effectiveness of the proposed loss function over the others.

assistant: text: We study the problem of multiset prediction and propose a novel multiset loss function, providing analysis and empirical evidence that demonstrates its effectiveness.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Large Scale Multi-Domain Multi-Task Learning with MultiModel

Paper content:
Deep learning yields great results across many fields,
from speech recognition, image classification, to translation.
But for each problem, getting a deep model to work well involves
research into the architecture and a long period of tuning.
We present a single model that yields good results on a number
of problems spanning multiple domains.
In particular, this single model
is trained concurrently on ImageNet, multiple translation tasks,
image captioning (COCO dataset), a speech recognition corpus,
and an English parsing task.
Our model architecture incorporates building blocks from multiple
domains.
It contains convolutional layers, an attention mechanism,
and sparsely-gated layers.
Each of these computational blocks is crucial for a subset of
the tasks we train on.
Interestingly, even if a block is not
crucial for a task, we observe that adding it never hurts performance
and in most cases improves it on all tasks.
We also show that tasks with less data benefit largely from joint
training with other tasks, while performance on large tasks degrades
only slightly if at all.

assistant: text: Large scale multi-task architecture solves ImageNet and translation together and shows transfer learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach

Paper content:
Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences.
From practitioners point of view, we are usually interested in solving problems where tasks are learned sequentially, in a way that avoids revisiting all previous data at each stage.
We address this problem by introducing a conceptually simple and scalable end-to-end approach of incorporating past knowledge by learning prior directly from the data.
We consider scalable boosting-like approximation for intractable theoretical optimal prior.
We provide empirical studies on two commonly used benchmarks, namely  MNIST and Fashion MNIST on disjoint sequential image generation tasks.
For each dataset proposed method delivers the best results among comparable approaches,  avoiding catastrophic forgetting in a fully automatic way with a fixed model architecture.

assistant: text: Novel algorithm for Incremental learning of VAE with fixed architecture
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Paper content:
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction.
To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subspaces') in which adversarial examples lie.
We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID).
LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors.
We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the distinction of adversarial examples generated using state-of-the-art attacks.
As a proof-of-concept, we show that a potential application of LID is to distinguish adversarial examples, and the preliminary results show that it can outperform several state-of-the-art detection measures by large margins for five attack strategies considered in this paper across three benchmark datasets.
Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.

assistant: text: We characterize the dimensional properties of adversarial subspaces in the neighborhood of adversarial examples via the use of Local Intrinsic Dimensionality (LID).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks

Paper content:
To reduce memory footprint and run-time latency, techniques such as neural net-work pruning and binarization have been explored separately.
However, it is un-clear how to combine the best of the two worlds to get extremely small and efficient models
.  In this paper, we, for the first time, define the filter-level pruning problem for binary neural networks, which cannot be solved by simply migrating existing structural pruning methods for full-precision models
.  A novel learning-based approach is proposed to prune filters in our main/subsidiary network frame-work, where the main network is responsible for learning representative features to optimize the prediction performance, and the subsidiary component works as a filter selector on the main network
. To avoid gradient mismatch when training the subsidiary component, we propose a layer-wise and bottom-up scheme
.  We also provide the theoretical and experimental comparison between our learning-based and greedy rule-based methods
.  Finally, we empirically demonstrate the effectiveness of our approach applied on several binary models,  including binarizedNIN, VGG-11, and ResNet-18, on various image classification datasets
.  For bi-nary ResNet-18 on ImageNet, we use 78.6% filters but can achieve slightly better test error 49.87% (50.02%-0.15%) than the original model

assistant: text: we define the filter-level pruning problem for binary neural networks for the first time and propose method to solve it.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation

Paper content:
Surrogate models can be used to accelerate approximate Bayesian computation (ABC).
In one such framework the discrepancy between simulated and observed data is modelled with a Gaussian process.
So far principled strategies have been proposed only for sequential selection of the simulation locations.
To address this limitation, we develop Bayesian optimal design strategies to parallellise the expensive simulations.
We also touch the problem of quantifying the uncertainty of the ABC posterior due to the limited budget of simulations.

assistant: text: We propose principled batch Bayesian experimental design strategies and a method for uncertainty quantification of the posterior summaries in a Gaussian process surrogate-based approximate Bayesian computation framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data Augmentation in Training CNNs: Injecting Noise to Images

Paper content:
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks.
This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures.
Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison.
The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection.
The new approaches will provide better understanding on optimal learning procedures for image classification.

assistant: text: Ideal methodology to inject noise to input data during CNN training
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multilingual Alignment of Contextual Word Representations

Paper content:
We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in analyzing and improving multilingual BERT.
In particular, after our proposed alignment procedure, BERT exhibits significantly improved zero-shot performance on XNLI compared to the base model, remarkably matching pseudo-fully-supervised translate-train models for Bulgarian and Greek.
Further, to measure the degree of alignment, we introduce a contextual version of word retrieval and show that it correlates well with downstream zero-shot transfer.
Using this word retrieval task, we also analyze BERT and find that it exhibits systematic deficiencies, e.g. worse alignment for open-class parts-of-speech and word pairs written in different scripts, that are corrected by the alignment procedure.
These results support contextual alignment as a useful concept for understanding large multilingual pre-trained models.

assistant: text: We propose procedures for evaluating and strengthening contextual embedding alignment and show that they both improve multilingual BERT's zero-shot XNLI transfer and provide useful insights into the model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ordinary differential equations on graph networks

Paper content:
Recently various neural networks have been proposed for irregularly structured data such as graphs and manifolds.
To our knowledge, all existing graph networks have discrete depth.
Inspired by neural ordinary differential equation (NODE) for data in the Euclidean domain, we extend the idea of continuous-depth models to graph data, and propose graph ordinary differential equation (GODE).
The derivative of hidden node states are parameterized with a graph neural network, and the output states are the solution to this ordinary differential equation.
We demonstrate two end-to-end methods for efficient training of GODE: (1) indirect back-propagation with the adjoint method; (2) direct back-propagation through the ODE solver, which accurately computes the gradient.
We demonstrate that direct backprop outperforms the adjoint method in experiments.
We then introduce a family of bijective blocks, which enables $\mathcal{O}(1)$ memory consumption.
We demonstrate that GODE can be easily adapted to different existing graph neural networks and improve accuracy.
We validate the performance of GODE in both semi-supervised node classification tasks and graph classification tasks.
Our GODE model achieves a continuous model in time, memory efficiency, accurate gradient estimation, and generalizability with different graph networks.

assistant: text: Apply ordinary differential equation model on graph structured data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On importance-weighted autoencoders

Paper content:
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than standard variational autoencoders by optimising a multi-sample objective, i.e. an objective that is expressible as an integral over $K > 1$ Monte Carlo samples.
Unfortunately, IWAE crucially relies on the availability of reparametrisations and even if these exist, the multi-sample objective leads to inference-network gradients which break down as $K$ is increased (Rainforth et al., 2018).
This breakdown can only be circumvented by removing high-variance score-function terms, either by heuristically ignoring them (which yields the 'sticking-the-landing' IWAE (IWAE-STL) gradient from Roeder et al. (2017)) or through an identity from Tucker et al. (2019) (which yields the 'doubly-reparametrised' IWAE (IWAE-DREG) gradient).
In this work, we argue that directly optimising the proposal distribution in importance sampling as in the reweighted wake-sleep (RWS) algorithm from Bornschein & Bengio (2015) is preferable to optimising IWAE-type multi-sample objectives.
To formalise this argument, we introduce an adaptive-importance sampling framework termed adaptive importance sampling for learning (AISLE) which slightly generalises the RWS algorithm.
We then show that AISLE admits IWAE-STL and IWAE-DREG (i.e. the IWAE-gradients which avoid breakdown) as special cases.

assistant: text: We show that most variants of importance-weighted autoencoders can be derived in a more principled manner as special cases of adaptive importance-sampling approaches like the reweighted-wake sleep algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lifelong Learning via Online Leverage Score Sampling

Paper content:
In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for life-long learning, effectively utilizing the previously acquired skills.
As such, the key challenge is to transfer and generalize the knowledge learned from one task to other tasks, avoiding interference from previous knowledge and improving the overall performance.
In this paper, within the continual learning paradigm, we introduce a method that effectively forgets the less useful data samples continuously across different tasks.
The method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property.
This effectively maintains a constant training size across all tasks.
We first provide some mathematical intuition for the method and then demonstrate its effectiveness with experiments on variants of MNIST and CIFAR100 datasets.

assistant: text: A new method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Lipschitz Regularization

Paper content:
Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality.
However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques.
Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations.
Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.

assistant: text: alternative to gradient penalty
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GumbelClip: Off-Policy Actor-Critic Using Experience Replay

Paper content:
This paper presents GumbelClip, a set of modifications to the actor-critic algorithm, for off-policy reinforcement learning.
GumbelClip uses the concepts of truncated importance sampling along with additive noise to produce a loss function enabling the use of off-policy samples.
The modified algorithm achieves an increase in convergence speed and sample efficiency compared to on-policy algorithms and is competitive with existing off-policy policy gradient methods while being significantly simpler to implement.
The effectiveness of GumbelClip is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.

assistant: text: With a set of modifications, under 10 LOC, to A2C you get an off-policy actor-critic that outperforms A2C and performs similarly to ACER. The modifications are large batchsizes, aggressive clamping, and policy "forcing" with gumbel noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Protein Family Classification using Deep Learning

Paper content:
Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm.
This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection  of  the  type  protein  representation  is  challenging  based  on  which  output  in  terms  ofaccuracy is depended on, The protein representation used here is n-gram i.e.  3-gram and Kerasembedding used for biological sequences like protein.
In this paper we are working on the Proteinclassification to show the strength and representation of biological sequence of the proteins

assistant: text: Protein Family Classification using Deep Learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Weighted Representations for Generalization Across Designs

Paper content:
Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to machine learning applications.
One example is the estimation of treatment effects from observational data, where a subtask is to predict the effect of a treatment on subjects that are systematically different from those who received the treatment in the data.
A related kind of distributional shift appears in unsupervised domain adaptation, where we are tasked with generalizing to a distribution of inputs that is different from the one in which we observe labels.
We pose both of these problems as prediction under a shift in design.
Popular methods for overcoming distributional shift are often heuristic or rely on assumptions that are rarely true in practice, such as having a well-specified model or knowing the policy that gave rise to the observed data.
Other methods are hindered by their need for a pre-specified metric for comparing observations, or by poor asymptotic properties.
In this work, we devise a bound on the generalization error under design shift, based on integral probability metrics and sample re-weighting.
We combine this idea with representation learning, generalizing and tightening existing results in this space.
Finally, we propose an algorithmic framework inspired by our bound and verify is effectiveness in causal effect estimation.

assistant: text: A theory and algorithmic framework for prediction under distributional shift, including causal effect estimation and domain adaptation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer

Paper content:
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification.
Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm.
This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached.
Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene.
As such, we propose a novel evaluation measure, parametric norm-balls, by directly perturbing physical parameters that underly image formation.
One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry.
Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.
Research in adversarial examples continues to contribute to the development of robust (semi-)supervised learning (Miyato et al., 2018) , data augmentation BID14 Sun et al., 2018) , and machine learning understanding BID24 .
One important caveat of the approach pursued by much of the literature in adversarial machine learning, as discussed recently (Goodfellow, original image parametric (lighting) texture color [Athalye 17] multi-step pixel [Moosavi Dezfooli 16] parametric (geometry) one-step pixel [Goodfellow 14] Figure 1: Traditional pixel-based adversarial attacks yield unrealistic images under a larger perturbation (L ∞ -norm ≈ 0.82), however our parametric lighting and geometry perturbations output more realistic images under the same norm (more results in Appendix A).

assistant: text: Enabled by a novel differentiable renderer, we propose a new metric that has real-world implications for evaluating adversarial machine learning algorithms, resolving the lack of realism of the existing metric based on pixel norms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension

Paper content:
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.
First, it puts forward a novel concept of "History of Word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation.
Second, it identifies an attention scoring function that better utilizes the "history of word" concept.
Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer.
We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017).
Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.

assistant: text: We propose a light-weight enhancement for attention and a neural architecture, FusionNet, to achieve SotA on SQuAD and adversarial SQuAD.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A DIRT-T Approach to Unsupervised Domain Adaptation

Paper content:
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space.
However, domain adversarial training faces two critical limitations:
1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint,
2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain.
In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions.
We propose two novel and related models:
1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes the violation the cluster assumption;
2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T) model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation.
Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks.

assistant: text: SOTA on unsupervised domain adaptation by leveraging the cluster assumption.