pwc-archive/papers-with-abstracts huggingface dataset:
V1Net: A computational model of cortical horizontal connections
Authors: Vijay Veerabadran, Virginia R. de Sa
Date: 2019-09-25 00:00:00
Tasks: Boundary Detection, model, Object Recognition

The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes. Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc. A key architectural feature combating such environmental irregularities is ‘long-range horizontal connections’ that aid the perception of the global form of objects. In this work, we explore the introduction of such horizontal connections into standard deep convolutional networks; we present V1Net -- a novel convolutional-recurrent unit that models linear and nonlinear horizontal inhibitory and excitatory connections inspired by primate visual cortical connectivity. We introduce the Texturized Challenge -- a new benchmark to evaluate object recognition performance under perceptual noise -- which we use to evaluate V1Net against an array of carefully selected control models with/without recurrent processing. Additionally, we present results from an ablation study of V1Net demonstrating the utility of diverse neurally inspired horizontal connections for state-of-the-art AI systems on the task of object boundary detection from natural images. We also present the emergence of several biologically plausible horizontal connectivity patterns, namely center-on surround-off, association fields and border-ownership connectivity patterns in a V1Net model trained to perform boundary detection on natural images from the Berkeley Segmentation Dataset 500 (BSDS500). Our findings suggest an increased representational similarity between V1Net and biological visual systems, and highlight the importance of neurally inspired recurrent contextual processing principles for learning visual representations that are robust to perceptual noise and furthering the state-of-the-art in computer vision.
INTERNAL-CONSISTENCY CONSTRAINTS FOR EMERGENT COMMUNICATION
Authors: Charles Lovering, Ellie Pavlick
Date: 2019-09-25 00:00:00

When communicating, humans rely on internally-consistent language representations. That is, as speakers, we expect listeners to behave the same way we do when we listen. This work proposes several methods for encouraging such internal consistency in dialog agents in an emergent communication setting. We consider two hypotheses about the effect of internal-consistency constraints: 1) that they improve agents’ ability to refer to unseen referents, and 2) that they improve agents’ ability to generalize across communicative roles (e.g. performing as a speaker de- spite only being trained as a listener). While we do not find evidence in favor of the former, our results show significant support for the latter.
Neural Networks for Principal Component Analysis: A New Loss Function Provably Yields Ordered Exact Eigenvectors
Authors: Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan Shell
Date: 2019-09-25 00:00:00
Tasks: Decoder

In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs). Optimizing the standard L2 loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but fails to identify the exact eigenvectors. This downside originates from an invariance that cancels out in the global map. Here, we prove that our loss function eliminates this issue, i.e. the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix. For this new loss, we establish that all local minima are global optima and also show that computing the new loss (and also its gradients) has the same order of complexity as the classical loss. We report numerical results on both synthetic simulations, and a real-data PCA experiment on MNIST (i.e., a 60,000 x784 matrix), demonstrating our approach to be practically applicable and rectify previous LAEs' downsides.
DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine
Authors: Qiang Sun, Zhinan Cheng, Yanwei Fu, Wenxuan Wang, Yu-Gang Jiang, xiangyang xue
Date: 2019-09-25 00:00:00
Tasks: Click-Through Rate Prediction

Click Through Rate (CTR) prediction is a critical task in industrial applications, especially for online social and commerce applications. It is challenging to find a proper way to automatically discover the effective cross features in CTR tasks. We propose a novel model for CTR tasks, called Deep neural networks with Encoder enhanced Factorization Machine (DeepEnFM). Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields. The embeddings generated from encoder are beneficial for the further feature interactions. Particularly, DeepEnFM utilizes a bilinear approach to generate different similarity functions with respect to different field pairs. Furthermore, the max-pooling method makes DeepEnFM feasible to capture both the supplementary and suppressing information among different attention heads. Our model is validated on the Criteo and Avazu datasets, and achieves state-of-art performance.
Using Explainabilty to Detect Adversarial Attacks
Authors: Ohad Amosy and Gal Chechik
Date: 2019-09-25 00:00:00
Tasks: Robust classification

Deep learning models are often sensitive to adversarial attacks, where carefully-designed input samples can cause the system to produce incorrect decisions. Here we focus on the problem of detecting attacks, rather than robust classification, since detecting that an attack occurs may be even more important than avoiding misclassification. We build on advances in explainability, where activity-map-like explanations are used to justify and validate decisions, by highlighting features that are involved with a classification decision. The key observation is that it is hard to create explanations for incorrect decisions.  We propose EXAID, a novel attack-detection approach, which uses model explainability to identify images whose explanations are inconsistent with the predicted class. Specifically, we use SHAP, which uses Shapley values in the space of the input image, to identify which input features contribute to a class decision. Interestingly, this approach does not require to modify the attacked model, and it can be applied without modelling a specific attack. It can therefore be applied successfully to detect unfamiliar attacks, that were unknown at the time the detection model was designed. We evaluate EXAID on two benchmark datasets CIFAR-10 and SVHN, and against three leading attack techniques, FGSM, PGD and C&W. We find that EXAID improves over the SoTA detection methods by a large margin across a wide range of noise levels, improving detection from 70% to over 90% for small perturbations.
Random Bias Initialization Improving Binary Neural Network Training
Authors: Xinlin Li, Vahid Partovi Nia
Date: 2019-09-25 00:00:00

Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently. BNNs significantly reduce the computational cost, model size, and memory footprint.  However, there is still a performance gap between the successful full-precision neural network with ReLU activation and BNNs. We argue that the accuracy drop of BNNs is due to their geometry. 
We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart. This comparison suggests random bias initialization as a remedy to activation saturation in full-precision networks and  leads us towards an improved BNN training. Our numerical experiments confirm our geometric intuition.
SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model
Authors: Hideaki Hayashi, Seiichi Uchida
Date: 2019-09-25 00:00:00

In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning. This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components. The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner. Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity. Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.
ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE
Authors: Xinshao Wang, Yang Hua, Elyor Kodirov, Neil M. Robertson
Date: 2019-09-25 00:00:00
Tasks: Representation Learning

It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when semantically abnormal examples exist. Although great progress has been made, there is still one crucial research question which is not thoroughly explored yet: What training examples should be focused and how much more should they be emphasised to achieve robust learning? In this work, we study this question and propose gradient rescaling (GR) to solve it. GR modifies the magnitude of logit vector’s gradient to emphasise on relatively easier training data points when noise becomes more severe, which functions as explicit emphasis regularisation to improve the generalisation performance of DNNs. Apart from regularisation, we connect GR to examples weighting and designing robust loss functions. We empirically demonstrate that GR is highly anomaly-robust and outperforms the state-of-the-art by a large margin, e.g., increasing 7% on CIFAR100 with 40% noisy labels. It is also significantly superior to standard regularisers in both clean and abnormal settings. Furthermore, we present comprehensive ablation studies to explore the behaviours of GR under different cases, which is informative for applying GR in real-world scenarios.
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning
Authors: Yonathan Efroni, Manan Tomar, Mohammad Ghavamzadeh
Date: 2019-09-25 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Game of Go, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration. These algorithms iteratively solve short-horizon decision problems and converge to the optimal solution of the original one. By using model-free algorithms as solvers of the short-horizon problems we derive fully model-free algorithms which are instances of the multi-step DP framework. As model-free algorithms are prone to instabilities w.r.t. the decision problem horizon, this simple approach can help in mitigating these instabilities and results in an improved model-free algorithms. We test this approach and show results on both discrete and continuous control problems.
Hidden incentives for self-induced distributional shift
Authors: David Scott Krueger, Tegan Maharaj, Shane Legg, Jan Leike
Date: 2019-09-25 00:00:00
Tasks: BIG-bench Machine Learning, Meta-Learning

Decisions made by machine learning systems have increasing influence on the world. Yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in online learning for applications such as content recommendation, where the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users. Generally speaking, it is possible for an algorithm to change the distribution of its own inputs. We introduce the term self-induced distributional shift (SIDS) to describe this phenomenon. A large body of work in reinforcement learning and causal machine learning aims to deal with distributional shift caused by deploying learning systems previously trained offline. Our goal is similar, but distinct: we point out that changes to the learning algorithm, such as the introduction of meta-learning, can reveal hidden incentives for distributional shift (HIDS), and aim to diagnose and prevent problems associated with hidden incentives. We design a simple  environment as a "unit test" for HIDS, as well as a content recommendation environment which allows us to disentangle different types of SIDS.  We demonstrate the potential for HIDS to cause unexpected or undesirable behavior in these environments, and propose and test a mitigation strategy.
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning
Authors: Wenshan Wang, Yaoyu Hu, Sebastian Scherer
Date: 2019-09-25 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Applying reinforcement learning (RL) to real-world problems will require reasoning about action-reward correlation over long time horizons. Hierarchical reinforcement learning (HRL) methods handle this by dividing the task into hierarchies, often with hand-tuned network structure or pre-defined subgoals. We propose a novel HRL framework TAIC, which learns the temporal abstraction from past experience or expert demonstrations without task-specific knowledge. We formulate the temporal abstraction problem as learning latent representations of action sequences and present a novel approach of regularizing the latent space by adding information-theoretic constraints. Specifically, we maximize the mutual information between the latent variables and the state changes.
A visualization of the latent space demonstrates that our algorithm learns an effective abstraction of the long action sequences. The learned abstraction allows us to learn new tasks on higher level more efficiently. We convey a significant speedup in convergence over benchmark learning problems. These results demonstrate that learning temporal abstractions is an effective technique in increasing the convergence rate and sample efficiency of RL algorithms.
Temporal Difference Weighted Ensemble For Reinforcement Learning
Authors: Takuma Seno, Michita Imai
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Combining multiple function approximators in machine learning models typically leads to better performance and robustness compared with a single function. In reinforcement learning, ensemble algorithms such as an averaging method and a majority voting method are not always optimal, because each function can learn fundamentally different optimal trajectories from exploration. In this paper, we propose a Temporal Difference Weighted (TDW) algorithm, an ensemble method that adjusts weights of each contribution based on accumulated temporal difference errors. The advantage of this algorithm is that it improves ensemble performance by reducing weights of Q-functions unfamiliar with current trajectories. We provide experimental results for Gridworld tasks and Atari tasks that show significant performance improvements compared with baseline algorithms.
A Simple Technique to Enable Saliency Methods to Pass the Sanity Checks
Authors: Arushi Gupta, Sanjeev Arora
Date: 2019-09-25 00:00:00

 {\em Saliency methods} attempt to explain a deep net's decision by assigning a {\em score} to each feature/pixel in the input, often doing this credit-assignment via the gradient of the output with respect to input. 
Recently \citet{adebayosan} questioned the validity of many of these methods since they do not pass simple {\em sanity checks}, which test whether the scores shift/vanish when  layers of the trained net are randomized, or when the net is retrained using random labels for inputs. % for the inputs.   %Surprisingly, the tested methods did not pass these checks: the explanations were relatively unchanged. 

We propose a simple fix to existing saliency methods that helps them pass sanity checks, which we call {\em competition for pixels}. This involves computing saliency maps for all possible labels in the classification task, and using a simple competition among them to identify and remove less relevant pixels from the map. Some theoretical justification is provided for it  and its performance is empirically demonstrated on several popular methods.
Impact of the latent space on the ability of GANs to fit the distribution
Authors: Thomas Pinetz, Daniel Soukup, Thomas Pock
Date: 2019-09-25 00:00:00

The goal of generative models is to model the underlying data distribution of a
sample based dataset. Our intuition is that an accurate model should in principle
also include the sample based dataset as part of its induced probability distribution.
To investigate this, we look at fully trained generative models using the Generative
Adversarial Networks (GAN) framework and analyze the resulting generator
on its ability to memorize the dataset. Further, we show that the size of the initial
latent space is paramount to allow for an accurate reconstruction of the training
data. This gives us a link to compression theory, where Autoencoders (AE) are
used to lower bound the reconstruction capabilities of our generative model. Here,
we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
(2018)). Given a small latent space, the AE produces low quality and the GAN
produces high quality outputs from a perceptual viewpoint. In contrast, the distortion
error is smaller for the AE. By increasing the dimensionality of the latent
space the distortion decreases for both models, but the perceptual quality only
increases for the AE.
At Your Fingertips: Automatic Piano Fingering Detection
Authors: Amit Moryossef, Yanai Elazar, Yoav Goldberg
Date: 2019-09-25 00:00:00
Tasks: Generative Adversarial Network

Automatic Piano Fingering is a hard task which computers can learn using data. As data collection is hard and expensive, we propose to automate this process by automatically extracting fingerings from public videos and MIDI files, using computer-vision techniques. Running this process on 90 videos results in the largest dataset for piano fingering with more than 150K notes. We show that when running a previously proposed model for automatic piano fingering on our dataset and then fine-tuning it on manually labeled piano fingering data, we achieve state-of-the-art results.
In addition to the fingering extraction method, we also introduce a novel method for transferring deep-learning computer-vision models to work on out-of-domain data, by fine-tuning it on out-of-domain augmentation proposed by a Generative Adversarial Network (GAN).

For demonstration, we anonymously release a visualization of the output of our process for a single video on https://youtu.be/Gfs1UWQhr5Q
Latent Question Reformulation and Information Accumulation for Multi-Hop Machine Reading
Authors: Quentin Grail, Julien Perez, Eric Gaussier
Date: 2019-09-25 00:00:00
Tasks: Question Answering, Reading Comprehension

Multi-hop text-based question-answering is a current challenge in machine comprehension. 
This task requires to sequentially integrate facts from multiple passages to answer complex natural language questions.
In this paper, we propose a novel architecture, called the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.
LQR-net is composed of an association of \textbf{reading modules} and \textbf{reformulation modules}.
The purpose of the reading module is to produce a question-aware representation of the document.
From this document representation, the reformulation module extracts essential elements to calculate an updated representation of the question.
This updated question is then passed to the following hop.
We evaluate our architecture on the \hotpotqa question-answering dataset designed to assess multi-hop reasoning capabilities.
Our model achieves competitive results on the public leaderboard and outperforms the best current \textit{published} models in terms of Exact Match (EM) and $F_1$ score.
Finally, we show that an analysis of the sequential reformulations can provide interpretable reasoning paths.
Compressive Recovery Defense: A Defense Framework for $\ell_0, \ell_2$ and $\ell_\infty$ norm attacks.
Authors: Jasjeet Dhaliwal, Kyle Hambrook
Date: 2019-09-25 00:00:00

We provide recovery guarantees for compressible signals that have been corrupted with noise and extend the framework introduced in \cite{bafna2018thwarting} to defend neural networks against $\ell_0$, $\ell_2$, and $\ell_{\infty}$-norm attacks. In the case of $\ell_0$-norm noise, we provide recovery guarantees for Iterative Hard Thresholding (IHT) and Basis Pursuit (BP). For $\ell_2$-norm bounded noise, we provide recovery guarantees for BP, and for the case of $\ell_\infty$-norm bounded noise, we provide recovery guarantees for Dantzig Selector (DS). These guarantees theoretically bolster the defense framework introduced in \cite{bafna2018thwarting} for defending neural networks against adversarial inputs. Finally, we experimentally demonstrate the effectiveness of this defense framework against an array of $\ell_0$, $\ell_2$ and $\ell_\infty$-norm attacks.
REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH
Authors: Katsuki Ohto
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go. However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process. In this research, the coordination of episode generation was focused on. By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation. The experiments with a small problem showed that it had robust performance compared to the existing method, Alpha Zero.