huashen218/convxai-cia-dataset huggingface dataset:
Neural Sketch Learning for Conditional Program Generation
Keywords: Program generation, Source code, Program synthesis, Deep generative models
TL;DR: 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.

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.
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Keywords: reinforcement learning, nonstationarity, meta-learning, transfer learning, multi-agent

Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.
Learning Deep Mean Field Games for Modeling Large Population Behavior
Keywords: mean field games, reinforcement learning, Markov decision processes, inverse reinforcement learning, deep learning, inverse optimal control, computational social science, population modeling
TL;DR: Inference of a mean field game (MFG) model of large population behavior via a synthesis of MFG and Markov decision processes.

We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.
Spectral Normalization for Generative Adversarial Networks
Keywords: Generative Adversarial Networks, Deep Generative Models, Unsupervised Learning
TL;DR: We propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator of GANs.

One of the challenges in the study of generative adversarial networks is the instability of its training. 
        In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.
        Our new normalization technique is computationally light and easy to incorporate into existing implementations. 
        We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Keywords: Adversarial Subspace, Local Intrinsic Dimensionality, Deep Neural Networks
TL;DR: We characterize the dimensional properties of adversarial subspaces in the neighborhood of adversarial examples via the use of Local Intrinsic Dimensionality (LID).

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

The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Keywords: distributed sentence representations, multi-task learning
TL;DR: A large-scale multi-task learning framework with diverse training objectives to learn fixed-length sentence representations

A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. 
        We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Keywords: meta-learning, learning to learn, hierarchical Bayes, approximate Bayesian methods
TL;DR: 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.

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.
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Keywords: weight perturbation, reparameterization gradient, gradient variance reduction, evolution strategies, LSTM, regularization, optimization
TL;DR: We introduce flipout, an efficient method for decorrelating the gradients computed by stochastic neural net weights within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example.

Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Keywords: Reinforcement Learning, Exploration, Model-Free
TL;DR: We propose a generalization of visit-counters that evaluate the propagating exploratory value over trajectories, enabling efficient exploration for model-free RL

Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing.
        We propose E-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using E-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.
Implicit Causal Models for Genome-wide Association Studies
TL;DR: Implicit models applied to causality and genetics

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform the second best result by an absolute difference of 15-45.3%.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Keywords: 2000-Speaker Neural TTS, Monotonic Attention, Speech Synthesis

We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
Keywords: Recurrent Neural Networks, RNN, LSTM, Mobile Device, Sensors
TL;DR: We used an LSTM to detect when a smartphone walks into a building. Then we predict the device's floor level using data from sensors aboard the smartphone.

In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.
Divide and Conquer Networks
Keywords: Neural Networks, Combinatorial Optimization, Algorithms
TL;DR: Dynamic model that learns divide and conquer strategies by weak supervision.

We consider the learning of algorithmic tasks by mere observation of input-output
        pairs. Rather than studying this as a black-box discrete regression problem with
        no assumption whatsoever on the input-output mapping, we concentrate on tasks
        that are amenable to the principle of divide and conquer, and study what are its
        implications in terms of learning.
        This principle creates a powerful inductive bias that we leverage with neural
        architectures that are defined recursively and dynamically, by learning two scale-
        invariant atomic operations: how to split a given input into smaller sets, and how
        to merge two partially solved tasks into a larger partial solution. Our model can be
        trained in weakly supervised environments, namely by just observing input-output
        pairs, and in even weaker environments, using a non-differentiable reward signal.
        Moreover, thanks to the dynamic aspect of our architecture, we can incorporate
        the computational complexity as a regularization term that can be optimized by
        backpropagation. We demonstrate the flexibility and efficiency of the Divide-
        and-Conquer Network on several combinatorial and geometric tasks: convex hull,
        clustering, knapsack and euclidean TSP. Thanks to the dynamic programming
        nature of our model, we show significant improvements in terms of generalization
        error and computational complexity.
Monotonic Chunkwise Attention
Keywords: attention, sequence-to-sequence, speech recognition, document summarization
TL;DR: An online and linear-time attention mechanism that performs soft attention over adaptively-located chunks of the input sequence.

Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.
Variational Message Passing with Structured Inference Networks
Keywords: Variational Inference, Variational Message Passing, Variational Auto-Encoder, Graphical Models, Structured Models, Natural Gradients
TL;DR: We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model.

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.
All-but-the-Top: Simple and Effective Postprocessing for Word Representations

Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a {\em very simple}, and yet counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations {\em even stronger}. The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and text classification) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Keywords: Deep Learning, Non-convex Optmization, Generalization, Learning Theory, Neural Networks
TL;DR: We show that SGD learns two-layer over-parameterized neural networks with Leaky ReLU activations that provably generalize on linearly separable data.

Neural networks exhibit good generalization behavior in the
        over-parameterized regime, where the number of network parameters
        exceeds the number of observations. Nonetheless,
        current generalization bounds for neural networks fail to explain this
        phenomenon. In an attempt to bridge this gap, we study the problem of
        learning a two-layer over-parameterized neural network, when the data is generated by a linearly separable function. In the case where the network has Leaky
        ReLU activations, we provide both optimization and generalization guarantees for over-parameterized networks.
        Specifically, we prove convergence rates of SGD to a global
        minimum and provide generalization guarantees for this global minimum
        that are independent of the network size. 
        Therefore, our result clearly shows that the use of SGD for optimization both finds a global minimum, and avoids overfitting despite the high capacity of the model. This is the first theoretical demonstration that SGD can avoid overfitting, when learning over-specified neural network classifiers.