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

Title: Learning to Infer and Execute 3D Shape Programs

Paper content:
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.
In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.
In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.
Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.
After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.
Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.
It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.

assistant: text: An approach to infer shape programs given 3D models, with architecture consisting of a recurrent network that encodes a 3D shape and outputs instructions, and a second module that renders the program to 3D.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Not-So-Random Features

Paper content:
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.
Our method produces a sequence of feature maps, iteratively refining the SVM margin.
We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.
Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.
Choosing the right kernel is a classic question that has riddled machine learning practitioners and theorists alike.
Conventional wisdom instructs the user to select a kernel which captures the structure and geometric invariances in the data.
Efforts to formulate this principle have inspired vibrant areas of study, going by names from feature selection to multiple kernel learning (MKL).We
present a new, principled approach for selecting a translation-invariant or rotation-invariant kernel to maximize the SVM classification margin. We
first describe a kernel-alignment subroutine, which finds a peak in the Fourier transform of an adversarially chosen data-dependent measure. Then
, we define an iterative procedure that produces a sequence of feature maps, progressively improving the margin. The
resulting algorithm is strikingly simple and scalable. Intriguingly
, our analysis interprets the main algorithm as no-regret learning dynamics in a zero-sum min-max game, whose value is the classification margin. Thus, we are
able to quantify convergence guarantees towards the largest margin realizable by a kernel with the assumed invariance. Finally, we
exhibit experiments on synthetic and benchmark datasets, demonstrating consistent improvements over related random features-based kernel methods.
We have presented an efficient kernel learning method that uses tools from Fourier analysis and online learning to optimize over two natural infinite families of kernels.
With this method, we show meaningful improvements on benchmark tasks, compared to related random features-based methods.
Many theoretical questions remain, such as accelerating the search for Fourier peaks (e.g. Hassanieh et al. FORMULA1 ; Kapralov FORMULA1 ).
These, in addition to applying our learned kernels to state-of-the-art methods (e.g. convolutional kernel networks (Mairal et al., 2014; BID21 Mairal, 2016) ), prove to be exciting directions for future work.

assistant: text: The authors consider learning directly Fourier representations of shift/translation invariant kernels for machine learning applications with the alignment of the kernel to data as the objective function to optimize.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom

Paper content:
The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane.
In contrast, the optical zoom changes the focal length and the digital zoom is used to enlarge a certain region of an image to the original image size.
In this paper, we are the first to formulate an unsupervised 3D-zoom learning problem where images with an arbitrary zoom factor can be generated from a given single image.
An unsupervised framework is convenient, as it is a challenging task to obtain a 3D-zoom dataset of natural scenes due to the need for special equipment to ensure camera movement is restricted to the Z-axis.
Besides, the objects in the scenes should not move when being captured, which hinders the construction of a large dataset of outdoor scenes.
We present a novel unsupervised framework to learn how to generate arbitrarily 3D-zoomed versions of a single image, not requiring a 3D-zoom ground truth, called the Deep 3D-Zoom Net.
The Deep 3D-Zoom Net incorporates the following features:
(i) transfer learning from a pre-trained disparity estimation network via a back re-projection reconstruction loss;
(ii) a fully convolutional network architecture that models depth-image-based rendering (DIBR), taking into account high-frequency details without the need for estimating the intermediate disparity; and
(iii) incorporating a discriminator network that acts as a no-reference penalty for unnaturally rendered areas.
Even though there is no baseline to fairly compare our results, our method outperforms previous novel view synthesis research in terms of realistic appearance on large camera baselines.
We performed extensive experiments to verify the effectiveness of our method on the KITTI and Cityscapes datasets.

assistant: text: An algorithm for synthesizing 3D-zoom behavior when the camera is moving forward, a network structure incorporating disparity estimation in a GANs framework to synthesize novel views, and a proposed new computer vision task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual Energy-Based Models for Text Generation

Paper content:
Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.
The dominant parametric approach is based on locally normalized models which predict one word at a time.
While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process.
In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.
In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation.
Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa.
Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines.
Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.

assistant: text: We show that Energy-Based models when trained on the residual of an auto-regressive language model can be used effectively and efficiently to generate text.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semantic Code Repair using Neuro-Symbolic Transformation Networks

Paper content:
We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code.
The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated.
In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program.
Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs.
Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete.
Specifically, the architecture (1) generates a  shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space.
We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs.
Our model is able to predict the exact correct repair 41% of the time with a single guess, compared to 13% accuracy for an attentional sequence-to-sequence model.

assistant: text: Presents a neural network architecture consisting of the share, specialize and compete parts for repairing code in four cases.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FRICATIVE PHONEME DETECTION WITH ZERO DELAY

Paper content:
People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.
These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.
Fricative phonemes have an important part of their content concentrated in high frequency bands.
It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.
Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.
In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.
All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.

assistant: text: A deep learning based approach for zero delay fricative phoneme detection
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Sensitivity of Adversarial Robustness to Input Data Distributions

Paper content:
Neural networks are vulnerable to small adversarial perturbations.
Existing literature largely focused on understanding and mitigating the vulnerability of learned models.
In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.
Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.
Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier.
Empirical investigations further confirm our finding.
We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.
This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.
Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.

assistant: text: This paper investigates the origin of the lack of robustness of classifiers to perturbations of adversarial inputs under l-inf bounded perturbations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Step Size Optimization

Paper content:
This paper proposes a new approach for step size adaptation in gradient methods.
The proposed method called step size optimization (SSO) formulates the step size adaptation as an optimization problem which minimizes the loss function with respect to the step size for the given model parameters and gradients.
Then, the step size is optimized based on alternating direction method of multipliers (ADMM).
SSO does not require the second-order information or any probabilistic models for adapting the step size, so it is efficient and easy to implement.
Furthermore, we also introduce stochastic SSO for stochastic learning environments.
In the experiments, we integrated SSO to vanilla SGD and Adam, and they outperformed state-of-the-art adaptive gradient methods including RMSProp, Adam, L4-Adam, and AdaBound on extensive benchmark datasets.

assistant: text: We propose an efficient and effective step size adaptation method for the gradient methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding Isomorphism Bias in Graph Data Sets 

Paper content:
In recent years there has been a rapid increase in classification methods on graph structured data.
Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance.
However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set.
This prevents fair competition of the algorithms and raises a question of the validity of the obtained results.
We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.

assistant: text: Many graph classification data sets have duplicates, thus raising questions about generalization abilities and fair comparison of the models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies

Paper content:
We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
Given limited labeled data just before inference, those representations can also be associated with specific object types to perform classification.
To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM), which loosely models the function of a cortical column in the mammalian brain.
Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns and forgetting outliers, and top-down predictions.
We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examples per class.
STAMs suggest a promising direction to solve the UCL problem without catastrophic forgetting.

assistant: text: We introduce unsupervised continual learning (UCL) and a neuro-inspired architecture that solves the UCL problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Representations and Generative Models for 3D Point Clouds

Paper content:
Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling.
In this paper, we look at geometric data represented as point clouds.
We introduce a deep autoencoder (AE) network with excellent reconstruction quality and generalization ability.
The learned representations outperform the state of the art in 3D recognition tasks and enable basic shape editing applications via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation.
We also perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space our AEs and, Gaussian mixture models (GMM).
Interestingly, GMMs trained in the latent space of our AEs produce samples of the best fidelity and diversity.
To perform our quantitative evaluation of generative models, we propose simple measures of fidelity and diversity based on optimally matching between sets point clouds.

assistant: text: Deep autoencoders to learn a good representation for geometric 3D point-cloud data; Generative models for point clouds.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Study of a Simple, Expressive and Consistent Graph Feature Representation

Paper content:
Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.
To circumvent this problem and learn on graphs, graph feature representation is required.
Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.
While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).
We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.
In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.
To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.
Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.

assistant: text: This paper forcuses on the laplacian spectrum of a graph as means to generate a representation to be used to compare graphs and classify them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Approximability of Discriminators Implies Diversity in GANs

Paper content:
While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse.
The theoretical work of Arora et al. (2017a) suggests a dilemma about GANs’ statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse.
By contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators).
For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence.
This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes.
Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.

assistant: text: Proposes the notion of restricted approximability, and provides a sample complexity bound, polynomial in the dimension, which is useful in investigating lack of diversity in GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representing dynamically: An active process for describing sequential data

Paper content:
We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions.
The method simultaneously acquires representations of input data and its dynamics.
It is based on a hierarchical generative model composed of two levels.
In the first level, a model learns representations to generate observed data.
In the second level, representational states encode the dynamics of the lower one.
The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models.
The method actively explores the latent space guided by its knowledge and the uncertainty about it.
That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space.
So, no encoder or inference models are used since the generators also serve as their inverse transformations.
The method is evaluated in two scenarios, with static images and with videos.
The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders.
With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.

assistant: text: Combines neural networks and Gaussian distributions to create an architecture and generative model for images and video which minimizes the error between generated and supplied images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diversity-Sensitive Conditional Generative Adversarial Networks

Paper content:
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative  Adversarial  Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code.
To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes.
The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives.
Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity.
We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction.
We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

assistant: text: The paper proposes a regularization term for the conditional GAN objective in order to promote diverse multimodal generation and prevent mode collapse.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples

Paper content:
Neural networks make mistakes.
The reason why a mistake is made often remains a mystery.
As such neural networks often are considered a black box.
It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.
In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.
Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.
In this paper we explain our method and demonstrate it on MNIST and CelebA.
This approach could aid in demystifying neural networks for a user.

assistant: text: New way of explaining why a neural network has misclassified an image
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representation Learning with Multisets

Paper content:
We study the problem of learning permutation invariant representations that can capture containment relations.
We propose training a model on a novel task: predicting the size of the symmetric difference between pairs of multisets, sets which may contain multiple copies of the same object.
With motivation from fuzzy set theory, we formulate both multiset representations and how to predict symmetric difference sizes given these representations.
We model multiset elements as vectors on the standard simplex and multisets as the summations of such vectors, and we predict symmetric difference as the l1-distance between multiset representations.
We demonstrate that our representations more effectively predict the sizes of symmetric differences than DeepSets-based approaches with unconstrained object representations.
Furthermore, we demonstrate that the model learns meaningful representations, mapping objects of different classes to different standard basis vectors.

assistant: text: Based on fuzzy set theory, we propose a model that given only the sizes of symmetric differences between pairs of multisets, learns representations of such multisets and their elements.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Uncertainty in Multitask Transfer Learning

Paper content:
Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks.
This results in a rich prior capable of few-shot learning on new tasks.
The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments.
Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them.
Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning.
Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

assistant: text: Aims to learn a prior over neural networks for multiple tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Imitation Learning from Visual Data with Multiple Intentions

Paper content:
Recent advances in learning from demonstrations (LfD) with deep neural networks have enabled learning complex robot skills that involve high dimensional perception such as raw image inputs.
LfD algorithms generally assume learning from single task demonstrations.
In practice, however, it is more efficient for a teacher to demonstrate a multitude of tasks without careful task set up, labeling, and engineering.
Unfortunately in such cases, traditional imitation learning techniques fail to represent the multi-modal nature of the data, and often result in sub-optimal behavior.
In this paper we present an LfD approach for learning multiple modes of behavior from visual data.
Our approach is based on a stochastic deep neural network (SNN), which represents the underlying intention in the demonstration as a stochastic activation in the network.
We present an efficient algorithm for training SNNs, and for learning with vision inputs, we also propose an architecture that associates the intention with a stochastic attention module.
We demonstrate our method on real robot visual object reaching tasks, and show that
it can reliably learn the multiple behavior modes in the demonstration data.
Video results are available at https://vimeo.com/240212286/fd401241b9.

assistant: text: This paper shows how to learn several modalities using imitation learning from visual data using stochastic Neural Networks, and a method for learning from demonstrations where several modalities of the same task are given.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

Paper content:
Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge.
As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter.
The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge.
Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation.
We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks.
We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018).

assistant: text: A novel architecture for selecting knowledge-grounded multi-turn dialogue that yields state of the art on relevant benchmarks datasets, and scores higher in human evaluations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Branched Multi-Task Networks: Deciding What Layers To Share

Paper content:
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.
Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers.
Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome.
Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive.
In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities.
Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.
Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.

assistant: text: This paper presents a method to infer multi-task networks architecture to determine which part of the network should be shared among different tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Sample Complexity with Observational Supervision

Paper content:
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
In this context, we assess the utility of observational supervision, where we take advantage of passively-collected signals such as eye tracking or “gaze” data, to reduce the amount of hand-labeled data needed for model training.
Specifically, we leverage gaze information to directly supervise a visual attention layer by penalizing disagreement between the spatial regions the human labeler looked at the longest and those that most heavily influence model output.
We present evidence that constraining the model in this way can reduce the number of labeled examples required to achieve a given performance level by as much as 50%, and that gaze information is most helpful on more difficult tasks.

assistant: text: A method to incorporate gaze signals into standard CNNs for image classification, adding a loss function term based in the difference between the model's Class Activation Map and the map constructed from eye tracking information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An implicit function learning approach for parametric modal regression

Paper content:
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.
Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.
Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.
In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.
We empirically demonstrate on several synthetic problems that our method
(i) can learn multi-valued functions and produce the conditional modes,
(ii) scales well to high-dimensional inputs and
(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.
We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.

assistant: text: The present work proposes a parametric approach to estimate the conditional mode using the Implicit Function Theorem for multi-modal distributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Cost-to-go Estimates in Situated Temporal Planning

Paper content:
Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time.
In online planning, algorithms for real-time search or deadline-aware search have been considered before.
However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.
Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem.
In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner.
Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.

assistant: text: This paper addresses the problem of situated temporal planning, proposing a further simplification on  greedy strategies previously proposed by Shperberg.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Deep Representation Learning by Adaptive Latent Space Sampling

Paper content:
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance.
However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples.
In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation.
To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process.
The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task.
On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of our framework.

assistant: text: A method to efficiently select hard samples during neural network training, achieved via a variational auto-encoder that encodes samples into a latent space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Real or Fake: An Empirical Study and Improved Model for Fake Face Detection

Paper content:
Now GANs can generate more and more realistic face images that can easily fool human beings.
In contrast, a common convolutional neural network(CNN), e.g. ResNet-18, can achieve more than 99.9% accuracy in discerning fake/real faces if training and testing faces are from the same source.
In this paper, we performed both human studies and CNN experiments, which led us to two important findings.
One finding is that the textures of fake faces are substantially different from real ones.
CNNs can capture local image texture information for recognizing fake/real face, while such cues are easily overlooked by humans.
The other finding is that global image texture information is more robust to image editing and generalizable to fake faces from different GANs and datasets.
Based on the above findings, we propose  a  novel  architecture  coined  as  Gram-Net,  which  incorporates  “Gram Block” in multiple semantic levels to extract global image texture representations.
Experimental results demonstrate that our Gram-Net performs better than existing approaches for fake face detection.
Especially, our Gram-Net is more robust to image editing, e.g.  downsampling, JPEG compression, blur, and noise.
More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase.

assistant: text: An empirical study on fake images reveals that texture is an important cue that current fake images differ from real images. Our improved model capturing global texture statistics shows better cross-GAN fake image detection performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Paper content:
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency.
To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision.
Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models.
We also demonstrate ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks -- the highest scores to date.
Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary.
We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise.
The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates.
By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing.
Sensitivity analysis indicates that these techniques, coupled with proper activation function range calibration, offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.

assistant: text: A method for low bit quantization to enable inference on efficient hardware that achieves full accuracy on ResNet50 with 4-bit weights and activations, based on observations that fine-tuning at low precision introduces noise in the gradient.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

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

assistant: text: Training method to enforce strict constraints on learned embeddings during supervised training. Applied to visual question answering.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only

Paper content:
Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining.
Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest.
In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available.
Our main contributions are as follows.
First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier.
Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student).
Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words.
Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.

assistant: text: Discusses a variant of knowledge distillation which uses a "teacher" based on a bag-of-words classifier with seed words and a "student" which is an embedding-based neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FRICATIVE PHONEME DETECTION WITH ZERO DELAY

Paper content:
People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.
These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.
Fricative phonemes have an important part of their content concentrated in high frequency bands.
It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.
Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.
In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.
All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.

assistant: text: This paper apples supervised deep learning methods to detect exact duration of a fricative phoneme in order to improve practical frequency lowering algorithm.