pwc-archive/papers-with-abstracts huggingface dataset:
A Neuro-AI Interface: Learning DNNs from the Human Brain
Authors: Zhengwei Wang, Qi She, Eoin Brophy, Alan F. Smeaton, Tomas E. Ward, Graham Healy
Date: 2019-05-28 00:00:00
Tasks: Object Recognition, Open-Ended Question Answering

Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.  However, it is still an open question whether DNNs are able to make decisions like the brain. Previous work has demonstrated that DNNs, trained by matching the neural responses from inferior temporal (IT) cortex in monkey's brain, is able to achieve human-level performance on the image object recognition tasks. This indicates that neural dynamics can provide informative knowledge to help DNNs accomplish specific tasks. In this paper, we introduce the concept of a neuro-AI interface, which aims to use human's neural responses as supervised information for helping AI systems solve a task that is difficult when using traditional machine learning strategies. In order to deliver the idea of neuro-AI interfaces, we focus on deploying it to one of the fundamental problems in generative adversarial networks (GANs): designing a proper evaluation metric to evaluate the quality of images produced by GANs.
Simplicity bias in the parameter-function map of deep neural networks
Authors: Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis
Date: 2019-05-28 00:00:00

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.
Better Generalization with Adaptive Adversarial Training
Authors: Amit Despande, Sandesh Kamath, K V Subrahmanyam
Date: 2019-05-28 00:00:00
Tasks: Adversarial Robustness

An effective method to obtain an adversarial robust network is to train the network with adversarially perturbed samples. Perturbing all the samples adversarially has shown to increase the robustness of the networks significantly, but in turn affecting the generalization of the network to unperturbed points. We propose an adaptive training method which aims to perturb only a portion of the training samples which aids not only adversarial robustness but also better generalization as
compared to perturbing all the training samples. This method is also faster than perturbing the entire training set.
Evolution of Eigenvalue Decay in Deep Networks
Authors: Lukas Pfahler, Katharina Morik
Date: 2019-05-28 00:00:00

The linear transformations in converged deep networks show fast eigenvalue decay. The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.
We use a stochastic approximator to generate histograms of eigenvalues. This allows us to investigate layers with hundreds of thousands of dimensions. We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.
Do LSTMs Learn Compositionally?
Authors: Naomi Saphra, Adam Lopez
Date: 2019-05-28 00:00:00

LSTM-based language models exhibit compositionality in their representations, but how this behavior emerges over the course of training has not been explored. Analyzing synthetic data experiments with contextual decomposition, we find that LSTMs learn long-range dependencies compositionally by building them from shorter constituents during training.
Probing emergent geometry in speech models via replica theory
Authors: Suchismita Padhy, Jenelle Feather, Cory Stephenson, Oguz Elibol, Hanlin Tang, Josh Mcdermott, SueYeon Chung
Date: 2019-05-28 00:00:00
Tasks: speech-recognition, Speech Recognition

The success of deep neural networks in visual tasks have motivated recent theoretical and empirical work to understand how these networks operate. Meanwhile, deep neural networks have also achieved impressive performance in audio processing applications, both as sub-components of larger systems or as complete end-to-end systems. In this work, we employ a recently developed statistical mechanical theory that connects geometric properties of network representations with class separability to probe how information is untangled within neural networks trained to recognize speech. We find that speech recognition models carry out significant layerwise and temporal untangling of words by efficiently extracting task-relevant features. This untangling results from a decrease in the per-class radius and dimension, and a reduction in the correlation between class centers.
A Systematic Framework for Natural Perturbations from Videos
Authors: Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt
Date: 2019-05-28 00:00:00
Proceeding: ICML Workshop Deep_Phenomen 2019 6
Tasks: object-detection, Object Detection, Video Object Detection

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct ImageNet-Vid-Robust, a human-expert--reviewed dataset of 22,668 images grouped into 1,145 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16\%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions.
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data
Authors: Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young and Jason Dong
Date: 2019-05-28 00:00:00
Tasks: Classification, text-classification, Text Classification, Transfer Learning

Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embedding to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embedding like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification. Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.
Examining Interpretable Feature Relationships in Deep Networks for Action recognition
Authors: Mathew Monfort, Kandan Ramakrishnan, Barry A McNamara, Alex Lascelles, Dan Gutfreund, Rogerio Feris, Aude Oliva
Date: 2019-05-28 00:00:00
Tasks: Action Recognition

A number of recent methods to understand neural networks have focused on quantifying the role of individual features.  One such method, NetDissect identifies interpretable features of a model using the Broden dataset of visual semantic labels (colors, materials, textures, objects and scenes).  Given the recent rise of a number of action recognition datasets, we propose extending the Broden dataset to include actions to better analyze learned action models.  We describe the annotation process, results from interpreting action recognition models on the extended Broden dataset and examine interpretable feature paths to help us understand the conceptual hierarchy used to classify an action.
Garbage in, model out: Weight theft with just noise
Authors: Vinay Uday Prabhu, Nick Roberts, Matthew McAteer
Date: 2019-05-28 00:00:00

This paper explores the scenarios under which
an attacker can claim that ‘Noise and access to
the softmax layer of the model is all you need’
to steal the weights of a convolutional neural network
whose architecture is already known. We
were able to achieve 96% test accuracy using
the stolen MNIST model and 82% accuracy using
stolen KMNIST model learned using only
i.i.d. Bernoulli noise inputs. We posit that this
theft-susceptibility of the weights is indicative
of the complexity of the dataset and propose a
new metric that captures the same. The goal of
this dissemination is to not just showcase how far
knowing the architecture can take you in terms of
model stealing, but to also draw attention to this
rather idiosyncratic weight learnability aspects of
CNNs spurred by i.i.d. noise input. We also disseminate
some initial results obtained with using
the Ising probability distribution in lieu of the i.i.d.
Bernoulli distribution
Grassmannian initialization: Neural network initialization using sub-space packing
Authors: Vinay Uday Prabhu, Dian Ang Yap
Date: 2019-05-28 00:00:00

We recently observed that convolutional filters initialized
farthest apart from each other using offthe-
shelf pre-computed Grassmannian subspace
packing codebooks performed surprisingly well
across many datasets. Through this short paper,
we’d like to disseminate some initial results in this
regard in the hope that we stimulate the curiosity
of the deep-learning community towards considering
classical Grassmannian subspace packing
results as a source of new ideas for more efficient
initialization strategies.
Scaling characteristics of sequential multitask learning: Networks naturally learn to learn
Authors: Guy Davidson, Michael C. Mozer
Date: 2019-06-04 00:00:00
Proceeding: ICML Workshop Deep_Phenomen 2019 6

We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks.  This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter. Through simulations involving sequences of 10 related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts. We observed two key phenomena. First, forward facilitation---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, backward interference---the forgetting of the n previous tasks when learning task n+1---diminishes with n.  Forward facilitation is the goal of research on metalearning, and reduced backward interference is the goal of research on ameliorating catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.
Language-Conditioned Goal Generation: a New Approach to Language Grounding in RL
Authors: Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7
Tasks: Diversity, Instruction Following, Language Acquisition

In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between language and embodiment: how do learning agents connect or ground linguistic representations to the physical world ? This question has recently been approached by the Reinforcement Learning community under the framework of instruction-following agents. In these agents, behavioral policies or reward functions are conditioned on the embedding of an instruction expressed in natural language. This paper proposes another approach: using language to condition goal generators. Given any goal-conditioned policy, one could train a language-conditioned goal generator to generate language-agnostic goals for the agent. This method allows to decouple sensorimotor learning from language acquisition and enable agents to demonstrate a diversity of behaviors for any given instruction. We propose a particular instantiation of this approach and demonstrate its benefits.
Extended Abstract: Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Authors: Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh, Dhruv Batra
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7
Tasks: Vision and Language Navigation

Following a navigation instruction such as 'Walk down the stairs and stop near the sofa' requires an agent to ground scene elements referenced via language (e.g.'stairs') to visual content in the environment (pixels corresponding to 'stairs').

We ask the following question -- can we leverage abundant 'disembodied' web-scraped vision-and-language corpora (e.g. Conceptual Captions (Sharma et al., 2018)) to learn visual groundings (what do 'stairs' look like?) that improve performance on a relatively data-starved embodied perception task (Vision-and-Language Navigation)? Specifically, we develop VLN-BERT, a visiolinguistic transformer model that scores the compatibility between an instruction ('...stop near the sofa') and a sequence of panoramic images. We demonstrate that pretraining VLN-BERT on image-text pairs from the web significantly improves performance on VLN -- outperforming the prior state-of-the-art in the fully-observed setting by 4 absolute percentage points on success rate. Ablations of our pretraining curriculum show each stage to be impactful -- with their combination resulting in further synergistic effects.
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments – Extended Abstract
Authors: Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, Stefan Lee
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7
Tasks: Vision and Language Navigation

We develop a language-guided navigation task set in a continuous 3D environment where agents must execute low-level actions to follow natural language navigation directions. By being situated in continuous environments, this setting lifts a number of assumptions implicit in prior work that represents environments as a sparse graph of panoramas with edges corresponding to navigability. Specifically, our setting drops the presumptions of known environment topologies, short-range oracle navigation, and perfect agent localization. To contextualize this new task, we develop models that mirror many of the advances made in prior settings. We find significantly lower performance in the continuous setting -- suggesting that performance in topological settings may be inflated by the strong implicit assumptions.
Language-Goal Imagination to Foster Creative Exploration in Deep RL
Authors: Tristan Karch, Nicolas Lair, Cédric Colas, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, Pierre-Yves Oudeyer
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7

Developmental machine learning studies how artificial agents can model the way children learn open-ended repertoires of skills. Children are known to use language and its compositionality as a tool to imagine descriptions of outcomes they never experienced before and target them as goals during play. We introduce IMAGINE, an intrinsically motivated deep RL architecture that models this ability. Such imaginative agents, like children, benefit from the guidance of a social peer who provides language descriptions. To take advantage of goal imagination, agents must be able to leverage these descriptions to interpret their imagined goals. This generalization is made possible by modularity: a decomposition between learned goal-achievement reward function and policy relying on deep sets, gated attention and object-centered representations. We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity.
On the Relationship Between Structure in Natural Language and Models of Sequential Decision Processes
Authors: Roma Patel, Rafael Rodriguez-Sanchez, George Konidaris
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7
Tasks: Decision Making, Sequential Decision Making

Human language is distinguished by powerful semantics, rich structure, and incredible flexibility. It enables us to communicate with each other, thereby affecting the decisions we make and actions we take. While Artificial Intelligence (AI) has made great advances both in sequential decision-making  using Markov Decision Processes (MDPs) and in Natural Language Processing (NLP), the potential of language to inform  sequential decision-making  is still unrealized. We explore how the different functional elements of natural language---such as verbs, nouns and adjectives---relate to decision process formalisms of varying complexity and structure. We attempt to determine which elements of language can be usefully grounded to a particular class of decision process and how partial observability changes the usability of language information. Our work show that  more complex, structured models can capture linguistic concepts that simple MDPs cannot. We argue that the rich structure of natural language indicates that reinforcement learning should focus on richer, more highly structured models of decision-making.
Emergence of Multilingualism in Population based Referential Games
Authors: Shresth Verma
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7

The ability of agents to learn to communicate by interaction has been studied through emergent communication tasks. Inspired by previous work in this domain, we extend the referential game setup to a population of spatially distributed agents. In such a setting, our experiments reveal that multiple languages can emerge in the population and some agents develop multilingual traits. 
Further, an action-advising framework is proposed for improving sample efficiency in the learning process.
Emergence of compositional language in communication through noisy channel
Authors: Łukasz Kuciński, Paweł Kołodziej, Piotr Miłoś
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LaReL 2020 7

In this paper, we investigate how communication through a noisy channel can lead to the emergence of compositional language. Our approach is $\mbox{end-to-end}$, allows for different inductive biases on the agents’ architecture, and trains without periodical resets of the networks’ weights. This relaxes some of the assumptions in recently developed methods. The impact on the structure of the resulting language is shown in the context of signaling games. We also develop a new metric for measuring degree of compositionality.
Exchangeable Models in Meta Reinforcement Learning
Authors: Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control. The former component is often based on recurrent neural networks, which do not directly exploit the exchangeable structure of the inputs. We propose to use a lightweight, yet an expressive architecture that accounts for exchangeability. Combined with an off-policy reinforcement learning algorithm, it results in a meta-RL method that is sample-efficient, fast to train and able to quickly adapt to new test tasks as demonstrated on a couple of widely used benchmarks.
MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer Learning
Authors: Raj Ratn Pranesh, Ambesh Shekhar
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Language Modeling, Language Modelling, Sentiment Analysis, Transfer Learning

In the age of the internet, Memes have grown to be one of the hottest subjects on the internet and arguably. But despite their huge growth, there is not much attention towards meme sentimental analysis. In this paper, we present MemeSem- a multimodal deep neural network framework for sentiment analysis of memes via transfer learning. Our proposed model utilizes VGG19 pre-trained on ImageNet dataset and BERT language model to learn the visual and textual feature of the meme and combine them together to make predictions. We have performed a comparative analysis of MemeSem model with various baseline models. For our experiment, we prepared a dataset consisting of 10,115 internet memes with three sentiment classes- (Positive, Negative and Neutral). Our proposed model outperforms the baseline multimodals and independent unimodals based on either images or text. On an average MemeSem outperform the unimodal and multimodal baseline by 10.69\% and 3.41\%.
Tackling Non-forgetting and Forward Transfer with a Unified Lifelong Learning Approach
Authors: Xinyu Yun, Tanner A Bohn, Charles X. Ling
Date: 2020-06-12 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Few-Shot Learning, Lifelong learning

Humans are the best example of agents that can learn a variety of skills incrementally over the course of their lives, and imbuing machines with this skill is the goal of lifelong machine learning. Ideally, lifelong learning should achieve non-forgetting, forward and backward transfer, avoid confusion, support few-shot learning, and so on. In previous approaches, the focus has been given to subsets of these properties, often by fitting together with an array of separate mechanisms. In this work, we propose a simple yet powerful unified framework that supports almost all of these properties through {\em one} central consolidation mechanism. We then describe a particular instance of this framework designed to support non-forgetting and forward transfer. This novel approach works by efficiently locating sparse neural sub-networks and controlling their consolidation during lifelong learning.