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

Title: Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

Paper content:
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed.
Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies.
Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness.
To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task.
Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning.
We formulate this objective into a Bayesian inference framework and tackle it using variational inference.
We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches.
Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.

assistant: text: A novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning, and also class-specific learning within each task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control

Paper content:
Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks.
Prior works mostly focus on model-free adversarial attacks and agents with discrete actions.
In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics.
Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.

assistant: text: We study the problem of continuous control agents in deep RL with adversarial attacks and proposed a two-step algorithm based on learned model dynamics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Parsimonious Deep Feed-forward Networks

Paper content:
Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively.
However, the structure of standard feed-forward neural networks (FNNs) is simply a stack of fully connected layers, regardless of the feature correlations in data.
In addition, the number of layers and the number of neurons are manually tuned on validation data, which is time-consuming and may lead to suboptimal networks.
In this paper, we propose an unsupervised structure learning method for learning parsimonious deep FNNs.
Our method determines the number of layers, the number of neurons at each layer, and the sparse connectivity between adjacent layers automatically from data.
The resulting models are called Backbone-Skippath Neural Networks (BSNNs).
Experiments on 17 tasks show that, in comparison with FNNs,  BSNNs can achieve better or comparable classification performance with much fewer parameters.
The interpretability of BSNNs is also shown to be better than that of FNNs.

assistant: text: An unsupervised structure learning method for Parsimonious Deep Feed-forward Networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prototype Matching Networks for Large-Scale Multi-label  Genomic Sequence Classification

Paper content:
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs).
With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task.
There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as “motifs” and (2) interactions among TFs known as co-binding effects.
In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms.
Our PMN model automatically extracts prototypes (“motif”-like features) for each TF through a novel prototype-matching loss.
Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences.
On a reference TFBS dataset with 2.1 million genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically.
To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large scale TFBS prediction.
Not only is the proposed architecture accurate, but it also models the underlying biology.

assistant: text: We combine the matching network framework for few shot learning into a large scale multi-label model for genomic sequence classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DONUT: CTC-based Query-by-Example Keyword Spotting

Paper content:
Keyword spotting—or wakeword detection—is an essential feature for hands-free operation of modern voice-controlled devices.
With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword.
In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection.
The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording.
Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system.
DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.

assistant: text: We propose an interpretable model for detecting user-chosen wakewords that learns from the user's examples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Case for Object Compositionality in Deep Generative Models of Images

Paper content:
Deep generative models seek to recover the process with which the observed data was generated.
They may be used to synthesize new samples or to subsequently extract representations.
Successful approaches in the domain of images are driven by several core inductive biases.
However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked.
In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.
This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations.
We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level.
A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.

assistant: text: We propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ODIN: Outlier Detection In Neural Networks

Paper content:
Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand.
Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed.
We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring.
By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training.
We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.

assistant: text: An add-on method for deep learning to detect outliers during prediction-time
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Unsupervised Classification with Deep Generative Models

Paper content:
Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored.
Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes.
We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end.
We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model's latent representations.
We test our model's discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature.

assistant: text: Unsupervised classification via deep generative modeling with controllable feature learning evaluated in a difficult real world task
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-Modal Generative Adversarial Networks for Diverse Datasets

Paper content:
Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse.
In order to provide a better fit to the target data distribution when the dataset includes many different classes, we propose a variant of the basic GAN model, a Multi-Modal Gaussian-Mixture GAN (GM-GAN), where the probability distribution over the latent space is a mixture of Gaussians.
We also propose a supervised variant which is capable of conditional sample synthesis.
In order to evaluate the model's performance, we propose a new scoring method which separately takes into account two (typically conflicting) measures - diversity vs. quality of the generated data.
Through a series of experiments, using both synthetic and real-world datasets, we quantitatively show that GM-GANs outperform baselines, both when evaluated using the commonly used Inception Score, and when evaluated using our own alternative scoring method.
In addition, we qualitatively demonstrate how the unsupervised variant of GM-GAN tends to map latent vectors sampled from different Gaussians in the latent space to samples of different classes in the data space.
We show how this phenomenon can be exploited for the task of unsupervised clustering, and provide quantitative evaluation showing the superiority of our method for the unsupervised clustering of image datasets.
Finally, we demonstrate a feature which further sets our model apart from other GAN models: the option to control the quality-diversity trade-off by altering, post-training, the probability distribution of the latent space.
This allows one to sample higher quality and lower diversity samples, or vice versa, according to one's needs.

assistant: text: Multi modal Guassian distribution of latent space in GAN models improves performance and allows to trade-off quality vs. diversity
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Neural Networks as Gaussian Processes

Paper content:
It has long been known that a single-layer fully-connected neural network with an i.i.d.
prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.
This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of evaluating the corresponding GP.
Recently, kernel functions which mimic multi-layer random neural networks have been developed, but only outside of a Bayesian framework.
As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network.
In this work, we derive the exact equivalence between infinitely wide, deep, networks and GPs with a particular covariance function.
We further develop a computationally efficient pipeline to compute this covariance function.
We then use the resulting GP to perform Bayesian inference for deep neural networks on MNIST and CIFAR-10.
We observe that the trained neural network accuracy approaches that of the corresponding GP with increasing layer width, and that the GP uncertainty is strongly correlated with trained network prediction error.
We further find that test performance increases as finite-width trained networks are made wider and  more  similar  to  a  GP,  and  that  the  GP-based  predictions  typically  outperform  those  of  finite-width  networks.
Finally  we  connect  the  prior  distribution over weights and variances in our GP formulation to the recent development of signal propagation in random neural networks.

assistant: text: We show how to make predictions using deep networks, without training deep networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cross-Entropy Loss Leads To Poor Margins

Paper content:
Neural networks could misclassify inputs that are slightly different from their training data, which indicates a small margin between their decision boundaries and the training dataset.
In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.
In particular, we show that if the features of the training dataset lie in a low-dimensional affine subspace and the cross-entropy loss is minimized by using a gradient method, the margin between the training points and the decision boundary could be much smaller than the optimal value.
This result is contrary to the conclusions of recent related works such as (Soudry et al., 2018), and we identify the reason for this contradiction.
In order to improve the margin, we introduce differential training, which is a training paradigm that uses a loss function defined on pairs of points from each class.
We show that the decision boundary of a linear classifier trained with differential training indeed achieves the maximum margin.
The results reveal the use of cross-entropy loss as one of the hidden culprits of adversarial examples and introduces a new direction to make neural networks robust against them.

assistant: text: We show that minimizing the cross-entropy loss by using a gradient method could lead to a very poor margin if the features of the dataset lie on a low-dimensional subspace.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exploring Sentence Vectors Through Automatic Summarization

Paper content:
Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing.
However, relatively little work has explored the internal structure and properties of spaces of sentence vectors.
In this paper, we will explore the properties of sentence vectors by studying a particular real-world application: Automatic Summarization.
In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document.
In addition, we identify specific dimensions which are linked to effective summaries.
To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties.
We also compare the features of different methods of sentence embeddings.
Many of these insights have applications in uses of sentence embeddings far beyond summarization.

assistant: text: A comparison and detailed analysis of various sentence embedding models through the real-world task of automatic summarization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Censoring Representations with Multiple-Adversaries over Random Subspaces

Paper content:
Adversarial feature learning (AFL) is one of the promising ways for explicitly constrains neural networks to learn desired representations; for example, AFL could help to learn anonymized representations so as to avoid privacy issues.
AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary.
This paper proposes a novel design of the adversary, {\em multiple adversaries over random subspaces} (MARS) that instantiate the concept of the {\em volunerableness}.
The proposed method is motivated by an assumption that deceiving an adversary could fail to give meaningful information if the adversary is easily fooled, and adversary rely on single classifier suffer from this issues.
In contrast, the proposed method is designed to be less vulnerable, by utilizing the ensemble of independent classifiers where each classifier tries to predict sensitive variables from a different {\em subset} of the representations.
The empirical validations on three user-anonymization tasks show that our proposed method achieves state-of-the-art performances in all three datasets without significantly harming the utility of data.
This is significant because it gives new implications about designing the adversary, which is important to improve the performance of AFL.

assistant: text: This paper improves the quality of the recently proposed adversarial feature leaning (AFL) approach for incorporating explicit constrains to representations, by introducing the concept of the {\em vulnerableness} of the adversary.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Mixup Resynthesizers

Paper content:
In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders.
We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data.
Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label.
We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.

assistant: text: We leverage deterministic autoencoders as generative models by proposing mixing functions which combine hidden states from pairs of images. These mixes are made to look realistic through an adversarial framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Paper content:
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings.
We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep.
This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches.
Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, and it makes no assumptions about the action spaces of the agents.
As such, it is flexible enough to be applied to most multi-agent learning problems

assistant: text: We propose an approach to learn decentralized policies in multi-agent settings using attention-based critics and demonstrate promising results in environments with complex interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior

Paper content:
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy image.
The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image.
Given such a generator network, or prior, a noisy image can be denoised by finding the closest image in the range of the prior.
However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the networks parameters.
In this paper we consider the problem of denoising an image from additive Gaussian noise, assuming the image is well described by a deep neural network with ReLu activations functions, mapping a k-dimensional latent space to an n-dimensional image.
We state and analyze a simple gradient-descent-like iterative algorithm that minimizes a non-convex loss function, and provably removes a fraction of (1 - O(k/n)) of the noise energy.
We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.

assistant: text: By analyzing an algorithms minimizing a non-convex loss, we show that all but a small fraction of noise can be removed from an image using a deep neural network based generative prior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interactive Classification by Asking Informative Questions

Paper content:
We  propose an  interactive  classification approach  for natural  language  queries.
Instead of classifying given the natural language query only, we ask the user for additional information using a sequence of binary and multiple-choice questions.
At each turn, we use a policy controller to decide if to present a question or pro-vide the user the final answer, and select the best question to ask by maximizing the system information gain.
Our formulation enables bootstrapping the system without any interaction data, instead relying on non-interactive crowdsourcing an-notation tasks.
Our evaluation shows the interaction helps the system increase its accuracy and handle ambiguous queries, while our approach effectively balances the number of questions and the final accuracy.

assistant: text: We propose an interactive approach for classifying natural language queries by asking users for additional information using information gain and a reinforcement learning policy controller.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GRAPHS, ENTITIES, AND STEP MIXTURE

Paper content:
Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks.
Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation.
Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs.
To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM).
GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly.
These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations.
With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks.
Furthermore, we empirically demonstrate the significance of considering global information.
The source code will be publicly available in the near future.

assistant: text: Simple and effective graph neural network with mixture of random walk steps and attention
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Equilibrium Propagation with Continual Weight Updates

Paper content:
Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (BPTT), but with a learning rule local in space.
Given an input x and associated target y, EP proceeds in two phases: in the first phase neurons evolve freely towards a first steady state; in the second phase output neurons are nudged towards y until they reach a second steady state.
However, in existing implementations of EP, the learning rule is not local in time:
the weight update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.
This is a major impediment to the biological plausibility of EP and its efficient hardware implementation.
In this work, we propose a version of EP named Continual Equilibrium Propagation (C-EP) where neuron and synapse dynamics occur simultaneously throughout the second phase, so that the weight update becomes local in time.
We prove theoretically that, provided the learning rates are sufficiently small, at each time step of the second phase the dynamics of neurons and synapses follow the gradients of the loss given by BPTT (Theorem 1).
We demonstrate training with C-EP on MNIST and generalize C-EP to neural networks where neurons are connected by asymmetric connections.
We show through experiments that the more the network updates follows the gradients of BPTT, the best it performs in terms of training.
These results bring EP a step closer to biology while maintaining its intimate link with backpropagation.

assistant: text: We propose a continual version of Equilibrium Propagation, where neuron and synapse dynamics occur simultaneously throughout the second phase, with theoretical guarantees and numerical simulations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Anticipatory Asynchronous Advantage Actor-Critic (A4C): The power of Anticipation in Deep Reinforcement Learning

Paper content:
We propose to extend existing deep reinforcement learning  (Deep RL) algorithms by allowing them to additionally choose sequences of actions as a part of their policy.
This modification forces the network to anticipate the reward of action sequences, which, as we show, improves the exploration leading to better convergence.
Our proposal is simple, flexible, and can be easily incorporated into any Deep RL framework.
We show the power of our scheme by consistently outperforming the state-of-the-art GA3C algorithm on several popular Atari Games.

assistant: text: Anticipation improves convergence of deep reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach

Paper content:
Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.
In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem.
Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem.
The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.

assistant: text: We propose a neural framework that can learn to solve the Circuit Satisfiability problem from (unlabeled) circuit instances.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: End-to-End Entity and Event Extraction with Generative Adversarial Imitation Learning

Paper content:
We propose a new framework for entity and event extraction based on generative adversarial imitation learning -- an inverse reinforcement learning method using generative adversarial network (GAN).
We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse.
We utilize discriminators to estimate proper rewards according to the difference between the labels committed by ground-truth (expert) and the extractor (agent).
Experiments also demonstrate that the proposed framework outperforms state-of-the-art methods.

assistant: text: We use dynamic rewards to train event extractors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bridging the domain gap in cross-lingual document classification

Paper content:
The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages.
Cross-lingual understanding has made progress in this area using language universal representations.
However, most current approaches focus on the problem as one of aligning language and do not address the natural domain drift across languages and cultures.
In this paper, We address the domain gap in the setting of semi-supervised cross-lingual document classification, where labeled data is available in a source language and only unlabeled data is available in the target language.
We combine a state-of-the-art unsupervised learning method, masked language modeling pre-training, with a recent method for semi-supervised learning, Unsupervised Data Augmentation (UDA), to simultaneously close the language and the domain gap.
We show that addressing the domain gap in cross-lingual tasks is crucial.
We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification.

assistant: text: Semi-supervised Cross-lingual Document Classification
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Surprising Negative Results for Generative  Adversarial Tree Search 

Paper content:
While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics.
One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS)—a model-based method—with deep-Q networks (DQNs)—a model-free method.
MCTS requires generating rollouts, which is computationally expensive.
In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment.
Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN- based dynamics model and a reward predictor.
GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states.
Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions.
However, GATS fails to outperform DQNs in 4 out of 5 games.
Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds.
We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.

assistant: text: Surprising negative results on Model Based + Model deep RL
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Extreme Values are Accurate and Robust in Deep Networks

Paper content:
Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.
This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness.
We borrow the scale-space extreme value idea from SIFT, and propose EVPNet (extreme value preserving network) which contains three novel components to model the extreme values: (1) parametric differences of Gaussian (DoG) to extract extrema, (2) truncated ReLU to suppress non-stable extrema and (3) projected normalization layer (PNL) to mimic PCA-SIFT like feature normalization.
Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks (FGSM,PGD,etc) even without adversarial training.

assistant: text: This paper aims to leverage good properties of robust visual features like SIFT to renovate CNN architectures towards better accuracy and robustness.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition

Paper content:
End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER).
This suggests that predicting sequences of words directly may be helpful instead.
However, training with word-level supervision can be more difficult due to the sparsity of examples per label class.
In this paper we analyze an end-to-end ASR model that combines a word-and-character representation in a multi-task learning (MTL) framework.
We show that it improves on the WER and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically.
We find that by adding character-level supervision, the MTL model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model).

assistant: text: Multi-task learning improves word-and-character-level speech recognition by interpolating the preference biases of its components: frequency- and word length-preference.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Paper content:
Generating visualizations and interpretations from high-dimensional data is a
common problem in many fields.
Two key approaches for tackling this problem
are clustering and representation learning.
There are very performant deep
clustering models on the one hand and interpretable representation learning techniques,
often relying on latent topological structures such as self-organizing maps,
on the other hand.
However, current methods do not yet successfully combine
these two approaches.
We present a new deep architecture for probabilistic clustering,
VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM
modules connected by LSTM cells.
We show that they achieve superior
clustering performance compared to current deep clustering methods on static
MNIST/Fashion-MNIST data as well as medical time series, while inducing an
interpretable representation.
Moreover, on the medical time series, VarTPSOM
successfully predicts future trajectories in the original data space.

assistant: text: We present a new deep architecture, VarPSOM, and its extension to time series data, VarTPSOM,  which achieve superior clustering performance compared to current deep clustering methods on static and temporal data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Information lies in the eye of the beholder: The effect of representations on observed mutual information

Paper content:
Learning can be framed as trying to encode the mutual information between input and output while discarding other information in the input.
Since the distribution between input and output is unknown, also the true mutual information is.
To quantify how difficult it is to learn a task, we calculate a observed mutual information score by dividing the estimated mutual information by the entropy of the input.
We substantiate this score analytically by showing that the estimated mutual information has an error that increases with the entropy of the data.
Intriguingly depending on how the data is represented the observed entropy and mutual information can vary wildly.
There needs to be a match between how data is represented and how a model encodes it.
Experimentally we analyze image-based input data representations and demonstrate that performance outcomes of extensive network architectures searches are well aligned to the calculated score.
Therefore to ensure better learning outcomes, representations may need to be tailored to both task and model to align with the implicit distribution of the model.

assistant: text: We take a step towards measuring learning task difficulty and demonstrate that in practice performance strongly depends on the match of the representation of the information and the model interpreting it.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Game Changer: Accessible Audio and Tactile Guidance for Board and Card Games

Paper content:
Board games often rely on visual information such as the location of the game pieces and textual information on cards.
Due to this reliance on visual feedback, blind players are at a disadvantage because they cannot read the cards or see the location of the game pieces and may be unable to play a game without sighted help.
We present Game Changer, an augmented workspace that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.
In this paper, we describe the design of Game Changer and present findings from a user study in which 7 blind participants used Game Changer to play against a sighted partner.
Most players stated the game was more accessible with the additions from Game Changer and felt that Game Changer could be used to augment other games.

assistant: text: Game Changer is a system that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Finite Depth and Width Corrections to the Neural Tangent Kernel

Paper content:
We prove the precise scaling, at finite depth and width, for the mean and variance of the neural tangent kernel (NTK) in a randomly initialized ReLU network.
The standard deviation is exponential in the ratio of network depth to width.
Thus, even in the limit of infinite overparameterization, the NTK is not deterministic if depth and width simultaneously tend to infinity.
Moreover, we prove that for such deep and wide networks, the NTK has a non-trivial evolution during training by showing that the mean of its first SGD update is also exponential in the ratio of network depth to width.
This is sharp contrast to the regime where depth is fixed and network width is very large.
Our results suggest that, unlike relatively shallow and wide networks, deep and wide ReLU networks are capable of learning data-dependent features even in the so-called lazy training regime.

assistant: text: The neural tangent kernel in a randomly initialized ReLU net is non-trivial fluctuations as long as the depth and width are comparable.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

Paper content:
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection.
The performant systems, however, typically involve big models with numerous parameters.
Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems -- the models (often deep networks or wide networks or both) are compute and memory intensive.
Low precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models.
In this paper, we study the combination of these two techniques and show that the performance of low precision networks can be significantly improved by using knowledge distillation techniques.
We call our approach Apprentice and show state-of-the-art accuracies using ternary precision and 4-bit precision for many variants of ResNet architecture on ImageNet dataset.
We study three schemes in which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.

assistant: text: We show that knowledge transfer techniques can improve the accuracy of low precision networks and set new state-of-the-art accuracy for ternary and 4-bits precision.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Boo(n) for Evaluating Architecture Performance

Paper content:
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws.
Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling.
Reporting the best single model performance does not appropriately address this stochasticity.
We propose a normalized expected best-out-of-n performance (Boo_n) as a way to correct these problems.

assistant: text: We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Normalizing Constant Estimation with Gaussianized Bridge Sampling

Paper content:
Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate.
Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC).
We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant.
We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.

assistant: text: We develop a new method for normalization constant (Bayesian evidence) estimation using Optimal Bridge Sampling and a novel Normalizing Flow, which is shown to outperform existing methods in terms of accuracy and computational time.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dimensionality Reduction for Representing the Knowledge of Probabilistic Models

Paper content:
Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification.
However, the high dimensionality of these representations makes them difficult to interpret and prone to over-fitting.
We propose a simple, intuitive and scalable dimension reduction framework that takes into account the soft probabilistic interpretation of standard deep models for classification.
When applying our framework to visualization, our representations more accurately reflect inter-class distances than standard visualization techniques such as t-SNE.
We show experimentally that our framework improves generalization performance to unseen categories in zero-shot learning.
We also provide a finite sample error upper bound guarantee for the method.

assistant: text: dimensionality reduction for cases where examples can be represented as soft probability distributions