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

Title: Model-Ensemble Trust-Region Policy Optimization

Paper content:
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning.
However, they tend to suffer from high sample complexity, which hinders their use in real-world domains.
Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning.
In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.
To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process.
We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time.
Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks.

assistant: text: Deep Model-Based RL that works well.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Gradient-Based Meta-Learning Methods

Paper content:
We build a theoretical framework for understanding practical meta-learning methods that enables the integration of sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms in order to provide within-task performance guarantees.
Our approach improves upon recent analyses of parameter-transfer by enabling the task-similarity to be learned adaptively and by improving transfer-risk bounds in the setting of statistical learning-to-learn.
It also leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure.

assistant: text: Practical adaptive algorithms for gradient-based meta-learning with provable guarantees.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Message Passing with Structured Inference Networks

Paper content:
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret.
We propose a variational message-passing algorithm for variational inference in such models.
We make three contributions.
First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE).
Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE.
Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference.
By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.

assistant: text: We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning

Paper content:
Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension.
It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text.
In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations.
As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text.
In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set.
Empirical results show that the state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set.
However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.

assistant: text: We introduce ReClor, a reading comprehension dataset requiring logical reasoning, and find that current state-of-the-art models struggle with real logical reasoning with poor performance near that of random guess.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments

Paper content:
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.
In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW.
Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.

assistant: text: NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TarMAC: Targeted Multi-Agent Communication

Paper content:
We explore the collaborative multi-agent setting where a team of deep reinforcement learning agents attempt to solve a shared task in partially observable environments.
In this scenario, learning an effective communication protocol is key.
We propose a communication protocol that allows for targeted communication, where agents learn \emph{what} messages to send and \emph{who} to send them to.
Additionally, we introduce a multi-stage communication approach where the agents co-ordinate via several rounds of communication before taking an action in the environment.
We evaluate our approach on several cooperative multi-agent tasks, of varying difficulties with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to complex 3D indoor environments.
We demonstrate the benefits of targeted as well as multi-stage communication.
Moreover, we show that the targeted communication strategies learned by the agents are quite interpretable and intuitive.

assistant: text: Targeted communication in multi-agent cooperative reinforcement learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NADS: Neural Architecture Distribution Search for Uncertainty Awareness

Paper content:
Machine learning systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a different distribution from the one used for training.
With their growing use in critical applications, it becomes important to develop systems that are able to accurately quantify its predictive uncertainty and screen out these anomalous inputs.
However, unlike standard learning tasks, there is currently no well established guiding principle for designing architectures that can accurately quantify uncertainty.
Moreover, commonly used OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples.
To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS).
Unlike standard neural architecture search methods which seek for a single best performing architecture, NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify building blocks common among all uncertainty aware architectures.
With this formulation, we are able to optimize a stochastic outlier detection objective and construct an ensemble of models to perform OoD detection.
We perform multiple OoD detection experiments and observe that our NADS performs favorably compared to state-of-the-art OoD detection methods.

assistant: text: We propose an architecture search method to identify a distribution of architectures and use it to construct a Bayesian ensemble for outlier detection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Simplicity bias in the parameter-function map of deep neural networks

Paper content:
The idea that neural networks may exhibit a bias towards simplicity has a long history.
Simplicity bias provides a way to quantify this intuition.
It predicts, for a broad class of input-output maps which can describe many systems in science and engineering, that simple outputs are exponentially more likely to occur upon uniform random sampling of inputs than complex outputs are.
This simplicity bias behaviour has been observed for systems ranging from the RNA sequence to secondary structure map, to systems of coupled differential equations, to models of plant growth.
Deep neural networks can be viewed as a mapping from the space of parameters (the weights) to the space of functions (how inputs get transformed to  outputs by the network).
We show that this parameter-function map obeys the necessary  conditions for simplicity bias, and numerically show that it is hugely biased towards functions with low descriptional complexity.
We also demonstrate a Zipf like power-law probability-rank relation.
A bias towards simplicity may help explain why neural nets generalize so well.

assistant: text: A very strong bias towards simple outpouts is observed in many simple input-ouput maps. The parameter-function map of deep networks is found to be biased in the same way.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning a Meta-Solver for Syntax-Guided Program Synthesis

Paper content:
We study a general formulation of program synthesis called syntax-guided synthesis(SyGuS) that concerns synthesizing a program that follows a given grammar and satisfies a given logical specification.
Both the logical specification and the grammar have complex structures and can vary from task to task, posing significant challenges for learning across different tasks.
Furthermore, training data is often unavailable for domain specific synthesis tasks.
To address these challenges, we propose a meta-learning framework that learns a transferable policy from only weak supervision.
Our framework consists of three components:
1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network;
2) a grammar adaptive policy network which enables learning a transferable policy; and
3) a reinforcement learning algorithm that jointly trains the embedding and adaptive policy.
We evaluate the framework on 214 cryptographic circuit synthesis tasks.
It solves 141 of them in the out-of-box solver setting, significantly outperforming a similar search-based approach but without learning, which solves only 31.
The result is comparable to two state-of-the-art classical synthesis engines, which solve 129 and 153 respectively.
In the meta-solver setting, the framework can efficiently adapt to unseen tasks and achieves speedup ranging from 2x up to 100x.

assistant: text: We propose a meta-learning framework that learns a transferable policy from only weak supervision to solve synthesis tasks with different logical specifications and grammars.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Paper content:
Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved.
In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold.
In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality.
The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios.

assistant: text: We show that robust GAN priors work better than GAN priors for limited angle CT reconstruction which is a highly under-determined inverse problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data.
Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data.
However, learning non-parametric invariances directly from data remains an important open problem.
In this paper, we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself.
This layer can learn invariance to non-parametric transformations and interestingly, motivates and incorporates permanent random connectomes there by being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN).
PRC-NPTN networks are initialized with random connections (not just weights) which are a small subset of the connections in a fully connected convolution layer.
Importantly, these connections in PRC-NPTNs once initialized remain permanent throughout training and testing.
Random connectomes makes these architectures loosely more biologically plausible than many other mainstream network architectures which require highly ordered structures.
We motivate randomly initialized connections as a simple method to learn invariance from data itself while invoking invariance towards multiple nuisance transformations simultaneously.
We find that these randomly initialized permanent connections have positive effects on generalization, outperform much larger ConvNet baselines and the recently proposed Non-Parametric Transformation Network (NPTN) on benchmarks that enforce learning invariances from the data itself.
invariances directly from the data, with the only prior being the structure that allows them to do so.
with an enhanced ability to learn non-parametric invariances through permanent random connectivity.
we do not explore these biological connections in more detail, it is still an interesting observation.
The common presence of random connections in the cortex at a local level leads us to ask: Is it
Both train and test data were augmented leading to an increase in overall complexity of the problem.
No architecture was altered in anyway between the two transformations i.e. they were not designed Discussion.
We present all test errors for this experiment in Table.

assistant: text: A layer modelling local random connectomes in the cortex within deep networks capable of learning general non-parametric invariances from the data itself.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: INSTANCE CROSS ENTROPY FOR DEEP METRIC LEARNING

Paper content:
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed.
Some supervise the learning process by pairwise or tripletwise similarity constraints while others take the advantage of structured similarity information among multiple data points.
In this work, we approach deep metric learning from a novel perspective.
We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one.
ICE has three main appealing properties.
Firstly, similar to categorical cross entropy (CCE), ICE has clear probabilistic interpretation and exploits structured semantic similarity information for learning supervision.
Secondly, ICE is scalable to infinite training data as it learns on mini-batches iteratively and is independent of the training set size.
Thirdly, motivated by our relative weight analysis, seamless sample reweighting is incorporated.
It rescales samples’ gradients to control the differentiation degree over training examples instead of truncating them by sample mining.
In addition to its simplicity and intuitiveness, extensive experiments on three real-world benchmarks demonstrate the superiority of ICE.

assistant: text: We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors

Paper content:
Bayesian inference offers a theoretically grounded and general way to train neural networks and can potentially give calibrated uncertainty.
However, it is challenging to specify a meaningful and tractable prior over the network parameters, and deal with the weight correlations in the posterior.
To this end, this paper introduces two innovations:
(i) a Gaussian process-based hierarchical model for the network parameters based on recently introduced unit embeddings that can flexibly encode weight structures, and
(ii) input-dependent contextual variables for the weight prior that can provide convenient ways to regularize the function space being modeled by the network through the use of kernels.
We show these models provide desirable test-time uncertainty estimates, demonstrate cases of modeling inductive biases for neural networks with kernels and demonstrate competitive predictive performance on an active learning benchmark.

assistant: text: We introduce a Gaussian Process Prior over weights in a neural network and explore its ability to model input-dependent weights with benefits to various tasks, including uncertainty estimation and generalization in the low-sample setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visual Representation Learning with 3D View-Contrastive Inverse Graphics Networks

Paper content:
Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.
One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint?
Humans excel at this task.
Our ability to imagine and fill in missing visual information is tightly coupled with perception: we feel as if we see  the world in 3 dimensions, while in fact, information from only the front surface of the world hits our (2D) retinas.
This paper explores the connection between view-predictive representation learning and its role in the development of 3D visual recognition.
We propose inverse graphics networks, which take as input 2.5D video streams captured by a moving camera, and map to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera.
The model can also project its 3D feature maps to novel viewpoints, to predict and match against target views.
We propose contrastive prediction losses that can handle stochasticity of the visual input and can scale view-predictive learning to more photorealistic scenes than those considered in previous works.
We show that the proposed  model learns 3D visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating  motion of the inferred 3D feature maps in videos of dynamic scenes.
To the best of our knowledge, this is the first work that empirically shows view prediction to be a useful and scalable self-supervised task beneficial to 3D object detection.

assistant: text: We show that with the right loss and architecture, view-predictive learning improves 3D object detection
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Non-Parametric Invariances from Data with Permanent Random Connectomes 

Paper content:
One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data.
Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data.
However, learning non-parametric invariances directly from data remains an important open problem.
In this paper, we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself.
This layer can learn invariance to non-parametric transformations and interestingly, motivates and incorporates permanent random connectomes there by being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN).
PRC-NPTN networks are initialized with random connections (not just weights) which are a small subset of the connections in a fully connected convolution layer.
Importantly, these connections in PRC-NPTNs once initialized remain permanent throughout training and testing.
Random connectomes makes these architectures loosely more biologically plausible than many other mainstream network architectures which require highly ordered structures.
We motivate randomly initialized connections as a simple method to learn invariance from data itself while invoking invariance towards multiple nuisance transformations simultaneously.
We find that these randomly initialized permanent connections have positive effects on generalization, outperform much larger ConvNet baselines and the recently proposed Non-Parametric Transformation Network (NPTN) on benchmarks that enforce learning invariances from the data itself.

assistant: text: A layer modelling local random connectomes in the cortex within deep networks capable of learning general non-parametric invariances from the data itself.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalized Tensor Models for Recurrent Neural Networks

Paper content:
Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data.
However, this observed efficiency is not yet entirely explained by theory.
It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency --- a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN.
Such networks, however, are not very often applied to real life tasks.
In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency.
Our theoretical results are verified by a series of extensive computational experiments.

assistant: text: Analysis of expressivity and generality of recurrent neural networks with ReLu nonlinearities using Tensor-Train decomposition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding

Paper content:
Can the success of reinforcement learning methods for simple combinatorial optimization problems be extended to multi-robot sequential assignment planning?
In addition to the challenge of achieving near-optimal performance in large problems, transferability to an unseen number of robots and tasks is another key challenge for real-world applications.
In this paper, we suggest a method that achieves the first success in both challenges for robot/machine scheduling problems.
Our method comprises of three components.
First, we show any robot scheduling problem can be expressed as a random probabilistic graphical model (PGM).
We develop a mean-field inference method for random PGM and use it for Q-function inference.
Second, we show that transferability can be achieved by carefully designing two-step sequential encoding of problem state.
Third, we resolve the computational scalability issue of fitted Q-iteration by suggesting a heuristic auction-based Q-iteration fitting method enabled by transferability we achieved.
We apply our method to discrete-time, discrete space problems (Multi-Robot Reward Collection (MRRC)) and scalably achieve 97% optimality with transferability.
This optimality is maintained under stochastic contexts.
By extending our method to continuous time, continuous space formulation, we claim to be the first learning-based method with scalable performance in any type of multi-machine scheduling problems; our method scalability achieves comparable performance to popular metaheuristics in Identical parallel machine scheduling (IPMS) problems.

assistant: text: RL can solve (stochastic) multi-robot/scheduling problems scalably and transferably using graph embedding
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: “Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations

Paper content:
Neural Style Transfer has become a popular technique for
generating images of distinct artistic styles using convolutional neural networks.
This
recent success in image style transfer has raised the question of
whether similar methods can be leveraged to alter the “style” of musical
audio.
In this work, we attempt long time-scale high-quality audio transfer
and texture synthesis in the time-domain that captures harmonic,
rhythmic, and timbral elements related to musical style, using examples that
may have different lengths and musical keys.
We demonstrate the ability
to use randomly initialized convolutional neural networks to transfer
these aspects of musical style from one piece onto another using 3
different representations of audio: the log-magnitude of the Short Time
Fourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transform
spectrogram.
We propose using these representations as a way of
generating and modifying perceptually significant characteristics of
musical audio content.
We demonstrate each representation's
shortcomings and advantages over others by carefully designing
neural network structures that complement the nature of musical audio.
Finally, we show that the most
compelling “style” transfer examples make use of an ensemble of these
representations to help capture the varying desired characteristics of
audio signals.

assistant: text: We present a long time-scale musical audio style transfer algorithm which synthesizes audio in the time-domain, but uses Time-Frequency representations of audio.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GLAD: Learning Sparse Graph Recovery

Paper content:
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications.
A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation.
Many convex optimization algorithms have been designed to solve this formulation to recover the graph structure.
Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.
However, it is a challenging task in this case, since the symmetric positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in learned algorithms, and a direct mapping from data to precision matrix may contain many parameters.
We propose a deep learning architecture, GLAD, which uses an Alternating Minimization (AM) algorithm as our model inductive bias, and learns the model parameters via supervised learning.
We show that GLAD learns a very compact and effective model for recovering sparse graphs from data.

assistant: text: A data-driven learning algorithm based on unrolling the Alternating Minimization optimization for sparse graph recovery.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

Paper content:
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
Given a target compound, the task is to predict the likely chemical reactants to produce the target.
This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.
Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem.
Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions.
On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.

assistant: text: We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Sample-based Evaluation for Generative Adversarial Networks

Paper content:
In this paper, we propose an improved quantitative evaluation framework for Generative Adversarial Networks (GANs) on generating domain-specific images, where we improve conventional evaluation methods on two levels: the feature representation and the evaluation metric.
Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation.
Moreover, for datasets with multiple classes, we propose Class-Aware Frechet Distance (CAFD), which employs a Gaussian mixture model on the feature space to better fit the multi-manifold feature distribution.
Experiments and analysis on both the feature level and the image level were conducted to demonstrate improvements of our proposed framework over the recently proposed state-of-the-art FID method.
To our best knowledge, we are the first to provide counter examples where FID gives inconsistent results with human judgments.
It is shown in the experiments that our framework is able to overcome the shortness of FID and improves robustness.
Code will be made available.

assistant: text: This paper improves existing sample-based evaluation for GANs and contains some insightful experiments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Subgraph Isomorphism Counting

Paper content:
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.
Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem.
Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph.
To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting.
We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models.
Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy.
Our DIAMNet can further improve existing representation learning models for this more global problem.

assistant: text: In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adjustable Real-time Style Transfer

Paper content:
Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.
Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output.
Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum" for different input images.
Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization.
In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters.
These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image.
Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters.
We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.

assistant: text: Stochastic style transfer with adjustable features.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Sketch Learning for Conditional Program Generation

Paper content:
We study the problem of generating source code in a strongly typed,
Java-like programming language, given a label (for example a set of
API calls or types) carrying a small amount of information about the
code that is desired.
The generated programs are expected to respect a
`"realistic" relationship between programs and labels, as exemplified
by a corpus of labeled programs available during training.
Two challenges in such *conditional program generation* are that
the generated programs must satisfy a rich set of syntactic and
semantic constraints, and that source code contains many low-level
features that impede learning.
We address these problems by training
a neural generator not on code but on *program sketches*, or
models of program syntax that abstract out names and operations that
do not generalize across programs.
During generation, we infer a
posterior distribution over sketches, then concretize samples from
this distribution into type-safe programs using combinatorial
techniques.
We implement our ideas in a system for generating
API-heavy Java code, and show that it can often predict the entire
body of a method given just a few API calls or data types that appear
in the method.

assistant: text: We give a method for generating type-safe programs in a Java-like language, given a small amount of syntactic information about the desired code.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A closer look at the word analogy problem

Paper content:
Although word analogy problems have become a standard tool for evaluating word vectors, little is known about why word vectors are so good at solving these problems.
In this paper, I attempt to further our understanding of the subject, by developing a simple, but highly accurate generative approach to solve the word analogy problem for the case when all terms involved in the problem are nouns.
My results demonstrate the ambiguities associated with learning the relationship between a word pair, and the role of the training dataset in determining the relationship which gets most highlighted.
Furthermore, my results show that the ability of a model to accurately solve the word analogy problem may not be indicative of a model’s ability to learn the relationship between a word pair the way a human does.

assistant: text: Simple generative approach to solve the word analogy problem which yields insights into word relationships, and the problems with estimating them
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multitask learning of Multilingual Sentence Representations

Paper content:
We present a novel multi-task training approach to learning multilingual distributed representations of text.
Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model.
We construct sentence embeddings by processing word embeddings with an LSTM and by taking an average of the outputs.
Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone.
Our model shows competitive performance in a standard cross-lingual document classification task.
We also show the effectiveness of our method in a low-resource scenario.

assistant: text: We jointly train a multilingual skip-gram model and a cross-lingual sentence similarity model to learn high quality multilingual text embeddings that perform well in the low resource scenario.