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

Title: Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

Paper content:
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.
Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks.
Inspired by these insights, we push the limits of word embeddings even further.
We propose a novel fuzzy bag-of-words (FBoW) representation for text that contains all the words in the vocabulary simultaneously but with different degrees of membership, which are derived from similarities between word vectors.
We show that max-pooled word vectors are only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index rather than cosine similarity.
Finally, we propose DynaMax, a completely unsupervised and non-parametric similarity measure that dynamically extracts and max-pools good features depending on the sentence pair.
This method is both efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin and is even competitive with supervised word vectors trained to directly optimise cosine similarity.

assistant: text: Max-pooled word vectors with fuzzy Jaccard set similarity are an extremely competitive baseline for semantic similarity; we propose a simple dynamic variant that performs even better.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Paper content:
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks.
We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.
Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation.
Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected.
By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training.
To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure.
Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks.
Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.

assistant: text: We embed nodes in a graph as Gaussian distributions allowing us to capture uncertainty about their representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Single Episode Policy Transfer in Reinforcement Learning

Paper content:
Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL).
An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.
To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy.
This modular approach enables integration of state-of-the-art algorithms for variational inference or RL.
Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot.
In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.

assistant: text: Single episode policy transfer in a family of environments with related dynamics, via optimized probing for rapid inference of latent variables and immediate execution of a universal policy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sign Bits Are All You Need for Black-Box Attacks

Paper content:
We present a novel black-box adversarial attack algorithm with state-of-the-art model evasion rates for query efficiency under $\ell_\infty$ and $\ell_2$ metrics.
It exploits a \textit{sign-based}, rather than magnitude-based, gradient estimation approach that shifts the gradient estimation from continuous to binary black-box optimization.
It adaptively constructs queries to estimate the gradient, one query relying upon the previous, rather than re-estimating the gradient each step with random query construction.
Its reliance on sign bits yields  a smaller memory footprint and it requires neither hyperparameter tuning or dimensionality reduction.
Further, its theoretical performance is guaranteed and it can characterize  adversarial subspaces better than white-box gradient-aligned subspaces.
On two public black-box attack challenges and a model robustly trained against transfer attacks, the algorithm's evasion rates surpass all submitted attacks.
For a suite of published models,  the algorithm is $3.8\times$ less failure-prone while spending $2.5\times$  fewer queries versus the best combination of state of art algorithms.
For example, it evades a standard MNIST model using just $12$ queries on average.
Similar performance is observed on a standard IMAGENET model with an average of $579$ queries.

assistant: text: We present a sign-based, rather than magnitude-based, gradient estimation approach that shifts gradient estimation from continuous to binary black-box optimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Evaluation of Conditional GANs

Paper content:
Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains.
Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency, and intra-conditioning diversity.
In this setting, model benchmarking becomes a challenge, as each metric may indicate a different "best" model.
In this paper, we propose the Frechet Joint Distance (FJD), which is defined as the Frechet distance between joint distributions of images and conditioning, allowing it to implicitly capture the aforementioned properties in a single metric.
We conduct proof-of-concept experiments on a controllable synthetic dataset, which consistently highlight the benefits of FJD when compared to currently established metrics.
Moreover, we use the newly introduced metric to compare existing cGAN-based models for a variety of conditioning modalities (e.g. class labels, object masks, bounding boxes, images, and text captions).
We show that FJD can be used as a promising single metric for model benchmarking.

assistant: text: We propose a new metric for evaluating conditional GANs that captures image quality, conditional consistency, and intra-conditioning diversity in a single measure.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

Paper content:
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.
Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning.
We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training.
This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness.
Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes.
We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.

assistant: text: Applying the softmax function in training leads to indirect and unexpected supervision on features. We propose a new training objective to explicitly induce dense feature regions for locally sufficient samples to benefit adversarial robustness.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE

Paper content:
A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision.
The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data.
We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data.
Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time.
Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors.
However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks.
In this paper, we propose new model that can disentangle multiple dynamic factors.
Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors.
In experiments, we show that FAVAE can extract the disentangled dynamic factors.

assistant: text: We propose new model that can disentangle multiple dynamic factors in sequential data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding and Exploiting the Low-Rank Structure of Deep Networks

Paper content:
Training methods for deep networks are primarily variants on stochastic gradient descent.
Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts.
However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure.
This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent.
To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants.
There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets.
CR proved particularly successful in escaping plateau regions of the objective function.
We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.

assistant: text: We show that deep learning network derivatives have a low-rank structure, and this structure allows us to use second-order derivative information to calculate learning rates adaptively and in a computationally feasible manner.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ManiGAN: Text-Guided Image Manipulation

Paper content:
We propose a novel generative adversarial network for visual attributes manipulation (ManiGAN), which is able to semantically modify the visual attributes of given images using natural language descriptions.
The key to our method is to design a novel co-attention module to combine text and image information rather than simply concatenating two features along the channel direction.
Also, a detail correction module is proposed to rectify mismatched attributes of the synthetic image, and to reconstruct text-unrelated contents.
Finally, we propose a new metric for evaluating manipulation results, in terms of both the generation of text-related attributes and the reconstruction of text-unrelated contents.
Extensive experiments on benchmark datasets demonstrate the advantages of our proposed method, regarding the effectiveness of image manipulation and the capability of generating high-quality results.

assistant: text: We propose a novel method to manipulate given images using natural language descriptions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A new dog learns old tricks:  RL finds classic optimization algorithms

Paper content:
This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems.
Towards this goal, we introduce a number of key ideas from traditional algorithms and complexity theory.
First, we draw a new connection between primal-dual methods and reinforcement learning.
Next, we introduce the concept of adversarial distributions (universal and high-entropy training sets), which are distributions that encourage the learner to find algorithms that work well in the worst case.
We test our new ideas on a number of optimization problem such as the AdWords problem, the online knapsack problem, and the secretary problem.
Our results indicate that the models have learned behaviours that are consistent with the traditional optimal algorithms for these problems.
Machine learning has led to dramatic improvements in our capabilities to solve problems previously considered intractable.
Besides the obvious empirical evidence of success, there has also been a strong parallel effort in the theory of ML which aims to explain why, when, and how ML techniques work.Our goal in this paper is to explore whether machine learning can be used to learn algorithms for classic combinatorial optimization problems.
We will define this question more specifically by connecting to three concepts from traditional algorithms and complexity theory.
In this work, we introduced several ideas from traditional algorithmic thinking to train neural networks to solve online optimization problems.
In the problems that we consider, our results show that RL was able to find key characteristics of the optimal "pen-and-paper" algorithms.
However, in some instances (such as in the knapsack and secretary problem), we saw that some state augmentation was needed in order for the learner to more adequately recover the optimal algorithms.
In this work, we took a step towards that by having the RL environment encode that state in a form usable by the agent.
In future work, we plan to remove the state augmentation from the RL environment and force the agent to learn the state augmentation as part of the training process.
FIG3 compares the agent's learned algorithm with the optimal algorithm in the binary setting.
FIG3 plots the threshold for the agent's learned algorithm in the value setting with changing distributions.
Observe that both have learned a threshold at around 1/e.

assistant: text: By combining ideas from traditional algorithms design and reinforcement learning, we introduce a novel framework for learning algorithms that solve online combinatorial optimization problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Combining Q-Learning and Search with Amortized Value Estimates

Paper content:
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS).
In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.
The new Q-estimates are then used in combination with real experience to update the prior.
This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search.
SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari.
SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets.
By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.

assistant: text: We propose a model-based method called "Search with Amortized Value Estimates" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Classifier Synthesis for Generalized Few-Shot Learning

Paper content:
Object recognition in real-world requires handling long-tailed or even open-ended data.
An ideal visual system needs to reliably recognize the populated visual concepts and meanwhile efficiently learn about emerging new categories with a few training instances.
Class-balanced many-shot learning and few-shot learning tackle one side of this problem, via either learning strong classifiers for populated categories or learning to learn few-shot classifiers for the tail classes.
In this paper, we investigate the problem of generalized few-shot learning (GFSL) -- a model during the deployment is required to not only learn about "tail" categories with few shots, but simultaneously classify the "head" and "tail" categories.
We propose the Classifier Synthesis Learning (CASTLE), a learning framework that learns how to synthesize calibrated few-shot classifiers in addition to the multi-class classifiers of ``head'' classes, leveraging a shared neural dictionary.
CASTLE sheds light upon the inductive GFSL through optimizing one clean and effective GFSL learning objective.
It demonstrates superior performances than existing GFSL algorithms and strong baselines on MiniImageNet and TieredImageNet data sets.
More interestingly, it outperforms previous state-of-the-art methods when evaluated on standard few-shot learning.

assistant: text: We propose to learn synthesizing few-shot classifiers and many-shot classifiers using one single objective function for GFSL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient rescue of damaged neural networks

Paper content:
Neural networks in the brain and in neuromorphic chips confer systems with the ability to perform multiple cognitive tasks.
However, both kinds of networks experience a wide range of physical perturbations, ranging from damage to edges of the network to complete node deletions, that ultimately could lead to network failure.
A critical question is to understand how the computational properties of neural networks change in response to node-damage and whether there exist strategies to repair these networks in order to compensate for performance degradation.
Here, we study the damage-response characteristics of two classes of neural networks, namely multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) trained to classify images from MNIST and CIFAR-10 datasets respectively.
We also propose a new framework to discover efficient repair strategies to rescue damaged neural networks.
The framework involves defining damage and repair operators for dynamically traversing the neural networks loss landscape, with the goal of mapping its salient geometric features.
Using this strategy, we discover features that resemble path-connected attractor sets in the loss landscape.
We also identify that a dynamic recovery scheme, where networks are constantly damaged and repaired, produces a group of networks resilient to damage as it can be quickly rescued.
Broadly, our work shows that we can design fault-tolerant networks by applying on-line retraining consistently during damage for real-time applications in biology and machine learning.

assistant: text: strategy to repair damaged neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Safe Policy Learning for Continuous Control

Paper content:
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence.
We formulate these problems as {\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.
Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints.
Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data.
Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline.
We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.

assistant: text: A general framework for incorporating long-term safety constraints in policy-based reinforcement learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A comprehensive analysis on attention models

Paper content:
Sequence-to-sequence attention-based models are a promising approach for end-to-end speech recognition.
The increased model power makes the training procedure more difficult, and analyzing failure modes of these models becomes harder because of the end-to-end nature.
In this work, we present various analyses to better understand training and model properties.
We investigate on pretraining variants such as growing in depth and width, and their impact on the final performance, which leads to over 8% relative improvement in word error rate.
For a better understanding of how the attention process works, we study the encoder output and the attention energies and weights.
Our experiments were performed on Switchboard, LibriSpeech and Wall Street Journal.

assistant: text: improved pretraining, and analysing encoder output and attention
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy

Paper content:
Adversarial examples have somewhat disrupted the enormous success of machine learning (ML) and are causing concern with regards to its trustworthiness: A small perturbation of an input results in an arbitrary failure of an otherwise seemingly well-trained ML system.
While studies are being conducted to discover the intrinsic properties of adversarial examples, such as their transferability and universality, there is insufficient theoretic analysis to help understand the phenomenon in a way that can influence the design process of ML experiments.
In this paper, we deduce an information-theoretic model which explains adversarial attacks universally as the abuse of feature redundancies in ML algorithms.
We prove that feature redundancy is a necessary condition for the existence of adversarial examples.
Our model helps to explain the major questions raised in many anecdotal studies on adversarial examples.
Our theory is backed up by empirical measurements of the information content of benign and adversarial examples on both image and text datasets.
Our measurements show that typical adversarial examples introduce just enough redundancy to overflow the decision making of a machine learner trained on corresponding benign examples.
We conclude with actionable recommendations to improve the robustness of machine learners against adversarial examples.

assistant: text: A new theoretical explanation for the existence of adversarial examples
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MobileBERT: Task-Agnostic Compression of BERT by Progressive Knowledge Transfer

Paper content:
The recent development of Natural Language Processing (NLP) has achieved great success using large pre-trained models with hundreds of millions of parameters.
However, these models suffer from the heavy model size and high latency such that we cannot directly deploy them to resource-limited mobile devices.
In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model.
Like BERT, MobileBERT is task-agnostic; that is, it can be universally applied to various downstream NLP tasks via fine-tuning.
MobileBERT is a slimmed version of BERT-LARGE augmented with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
To train MobileBERT, we use a bottom-to-top progressive scheme to transfer the intrinsic knowledge of a specially designed Inverted Bottleneck BERT-LARGE teacher to it.
Empirical studies show that MobileBERT is 4.3x smaller and 4.0x faster than original BERT-BASE while achieving competitive results on well-known NLP benchmarks.
On the natural language inference tasks of GLUE, MobileBERT achieves 0.6 GLUE score performance degradation, and 367 ms latency on a Pixel 3 phone.
On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a 90.0/79.2 dev F1 score, which is 1.5/2.1 higher than BERT-BASE.

assistant: text: We develop a task-agnosticlly compressed BERT, which is 4.3x smaller and 4.0x faster than BERT-BASE while achieving competitive performance on GLUE and SQuAD.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Example Detection and Classification with Asymmetrical Adversarial Training

Paper content:
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the attacker is oblivious to the detection mechanism.
In this paper, we consider the adversarial detection problem under the robust optimization framework.
We partition the input space into subspaces and train adversarial robust subspace detectors using asymmetrical adversarial training (AAT).
The integration of the classifier and detectors presents a detection mechanism that provides a performance guarantee to the adversary it considered.
We demonstrate that AAT promotes the learning of class-conditional distributions, which further gives rise to generative detection/classification approaches that are both robust and more interpretable.
We provide comprehensive evaluations of the above methods, and demonstrate their competitive performances and compelling properties on adversarial detection and robust classification problems.

assistant: text: A new generative modeling technique based on asymmetrical adversarial training, and its applications to adversarial example detection and robust classification
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus

Paper content:
In recent years deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games.
Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces.
In this paper, we develop a novel policy gradient methodology for the case of large multidimensional discrete action spaces.
We propose two approaches for creating parameterized policies: LSTM parameterization and a Modified MDP (MMDP) giving rise to Feed-Forward Network (FFN) parameterization.
Both of these approaches provide expressive models to which backpropagation can be applied for training.
We then consider entropy bonus, which is typically added to the reward function to enhance exploration.
In the case of high-dimensional action spaces, calculating the entropy and the gradient of the entropy requires enumerating all the actions in the action space and running forward and backpropagation for each action, which may be computationally infeasible.
We develop several novel unbiased estimators for the entropy bonus and its gradient.
Finally, we test our algorithms on two environments: a multi-hunter multi-rabbit grid game and a multi-agent multi-arm bandit problem.

assistant: text: policy parameterizations and unbiased policy entropy estimators for MDP with large multidimensional discrete action space
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures

Paper content:
Log-linear models models are widely used in machine learning, and in particular are ubiquitous in deep learning architectures in the form of the softmax.
While exact inference and learning of these requires linear time, it can be done approximately in sub-linear time with strong concentrations guarantees.
In this work, we present LSH Softmax, a method to perform sub-linear learning and inference of the softmax layer in the deep learning setting.
Our method relies on the popular Locality-Sensitive Hashing to build a well-concentrated gradient estimator, using nearest neighbors and uniform samples.
We also present an inference scheme in sub-linear time for LSH Softmax using the Gumbel distribution.
On language modeling, we show that Recurrent Neural Networks trained with LSH Softmax perform on-par with computing the exact softmax while requiring sub-linear computations.

assistant: text: we present LSH Softmax, a softmax approximation layer for sub-linear learning and inference with strong theoretical guarantees; we showcase both its applicability and efficiency by evaluating on a real-world task: language modeling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bias Also Matters: Bias Attribution for Deep Neural Network Explanation

Paper content:
The gradient of a deep neural network (DNN) w.r.t. the input provides information that can be used to explain the output prediction in terms of the input features and has been widely studied to assist in interpreting DNNs.
In a linear model (i.e., $g(x)=wx+b$), the gradient corresponds solely to the weights $w$.
Such a model can reasonably locally linearly approximate a smooth nonlinear DNN, and hence the weights of this local model are the gradient.
The other part, however, of a local linear model, i.e., the bias $b$, is usually overlooked in attribution methods since it is not part of the gradient.
In this paper, we observe that since the bias in a DNN also has a non-negligible contribution to the correctness of predictions, it can also play a significant role in understanding DNN behaviors.
In particular, we study how to attribute a DNN's bias to its input features.
We propose a backpropagation-type algorithm ``bias back-propagation (BBp)'' that starts at the output layer and iteratively attributes the bias of each layer to its input nodes as well as combining the resulting bias term of the previous layer.
This process stops at the input layer, where summing up the attributions over all the input features exactly recovers $b$.
Together with the backpropagation of the gradient generating $w$, we can fully recover the locally linear model $g(x)=wx+b$.
Hence, the attribution of the DNN outputs to its inputs is decomposed into two parts, the gradient $w$ and the bias attribution, providing separate and complementary explanations.
We study several possible attribution methods applied to the bias of each layer in BBp.
In experiments, we show that BBp can generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions.

assistant: text: Attribute the bias terms of deep neural networks to input features by a backpropagation-type algorithm; Generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Searching for Stage-wise Neural Graphs In the Limit

Paper content:
Search space is a key consideration for neural architecture search.
Recently, Xie et al. (2019a) found that randomly generated networks from the same distribution perform similarly, which suggest we should search for random graph distributions instead of graphs.
We propose graphon as a new search space.
A graphon is the limit of Cauchy sequence of graphs and a scale-free probabilistic distribution, from which graphs of different number of vertices can be drawn.
This property enables us to perform NAS using fast, low-capacity models and scale the found models up when necessary.
We develop an algorithm for NAS in the space of graphons and empirically demonstrate that it can find stage-wise graphs that outperform DenseNet and other baselines on ImageNet.

assistant: text: Graphon is a good search space for neural architecture search and empirically produces good networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Black-Box Adversarial Attack with Transferable Model-based Embedding

Paper content:
We present a new method for black-box adversarial attack.
Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to attack an unknown target network.
The method produces adversarial perturbations with high level semantic patterns that are easily transferable.
We show that this approach can greatly improve the query efficiency of black-box adversarial attack across different target network architectures.
We evaluate our approach on MNIST, ImageNet and Google Cloud Vision API, resulting in a significant reduction on the number of queries.
We also attack adversarially defended networks on CIFAR10 and ImageNet, where our method not only reduces the number of queries, but also improves the attack success rate.

assistant: text: We present a new method that combines transfer-based and scored black-box adversarial attack, improving the success rate and query efficiency of black-box adversarial attack across different network architectures.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Code Synthesis with Priority Queue Training

Paper content:
We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards.
We introduce a novel iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far.
Then, we synthesize new programs and add them to the priority queue by sampling from the RNN.
We benchmark our algorithm called priority queue training (PQT) against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF.
Our experimental results show that our deceptively simple PQT algorithm significantly outperforms the baselines.
By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.

assistant: text: We use a simple search algorithm involving an RNN and priority queue to find solutions to coding tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph inference learning for semi-supervised classification

Paper content:
In this work, we address the semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures.
Recent works often solve this problem with the advanced graph convolution in a conventional supervised manner, but the performance could be heavily affected when labeled data is scarce.
Here we propose a Graph Inference Learning (GIL) framework to boost the performance of node classification by learning the inference of node labels on graph topology.
To bridge the connection of two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths and local topological structures together, which can make inference conveniently deduced from one node to another node.
For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted into test nodes.
Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed and NELL) demonstrate the superiority of our GIL when compared with other state-of-the-art methods in the semi-supervised node classification task.

assistant: text: We propose a novel graph inference learning framework by building structure relations to infer unknown node labels from those labeled nodes in an end-to-end way.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Paper content:
Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.
Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks.
Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model.
In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference.
Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm’s operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference.
We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.

assistant: text: A specific gradient-based meta-learning algorithm, MAML, is equivalent to an inference procedure in a hierarchical Bayesian model. We use this connection to improve MAML via methods from approximate inference and curvature estimation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Trellis Networks for Sequence Modeling

Paper content:
We present trellis networks, a new architecture for sequence modeling.
On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers.
On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.
Thus trellis networks with general weight matrices generalize truncated recurrent networks.
We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models.
Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention.
The code is available at https://github.com/locuslab/trellisnet .

assistant: text: Trellis networks are a new sequence modeling architecture that bridges recurrent and convolutional models and sets a new state of the art on word- and character-level language modeling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Alarm System for Segmentation Algorithm Based on Shape Model

Paper content:
It is usually hard for a learning system to predict correctly on the rare events, and there is no exception for segmentation algorithms.
Therefore, we hope to build an alarm system to set off alarms when the segmentation result is possibly unsatisfactory.
One plausible solution is to project the segmentation results into a low dimensional feature space, and then learn classifiers/regressors in the feature space to predict the qualities of segmentation results.
In this paper, we form the feature space using shape feature which is a strong prior information shared among different data, so it is capable to predict the qualities of segmentation results given different segmentation algorithms on different datasets.
The shape feature of a segmentation result is captured using the value of loss function when the segmentation result is tested using a Variational Auto-Encoder(VAE).
The VAE is trained using only the ground truth masks, therefore the bad segmentation results with bad shapes become the rare events for VAE and will result in large loss value.
By utilizing this fact, the VAE is able to detect all kinds of shapes that are out of the distribution of normal shapes in ground truth (GT).
Finally, we learn the representation in the one-dimensional feature space to predict the qualities of segmentation results.
We evaluate our alarm system on several recent segmentation algorithms for the medical segmentation task.
The segmentation algorithms perform differently on different datasets, but our system consistently provides reliable prediction on the qualities of segmentation results.

assistant: text: We use VAE to capture the shape feature for automatic segmentation evaluation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ShardNet: One Filter Set to Rule Them All

Paper content:
Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.
Machine learning theory implies that such networks are highly over-parameterised and that it should be possible to reduce their size without sacrificing accuracy, and indeed many recent studies have begun to highlight specific redundancies that can be exploited to achieve this.
In this paper, we take a further step in this direction by proposing a filter-sharing approach to compressing deep CNNs that reduces their memory footprint by repeatedly applying a single convolutional mapping of learned filters to simulate a CNN pipeline.
We show, via experiments on CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet that this allows us to reduce the parameter counts of networks based on common designs such as VGGNet and ResNet by a factor proportional to their depth, whilst leaving their accuracy largely unaffected.
At a broader level, our approach also indicates how the scale-space regularities found in visual signals can be leveraged to build neural architectures that are more parsimonious and interpretable.

assistant: text: We compress deep CNNs by reusing a single convolutional layer in an iterative manner, thereby reducing their parameter counts by a factor proportional to their depth, whilst leaving their accuracies largely unaffected
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Variational Video Prediction

Paper content:
Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging.
Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world.
Many existing methods tackle this problem by making simplifying assumptions about the environment.
One common assumption is that the outcome is deterministic and there is only one plausible future.
This can lead to low-quality predictions in real-world settings with stochastic dynamics.
In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables.
To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video.
We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned.
We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.
Our SV2P implementation will be open sourced upon publication.

assistant: text: Stochastic variational video prediction in real-world settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Defending Against Physically Realizable Attacks on Image Classification

Paper content:
We study the problem of defending deep neural network approaches for image classification from physically realizable attacks.
First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks.
Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples.
Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.

assistant: text: Defending Against Physically Realizable Attacks on Image Classification