OpenRaiser/Intern-Atlas huggingface dataset:
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
Venue: arXiv cs.HC 2021
Year: 2021

Governments are increasingly turning to algorithmic risk assessments when making important decisions, such as whether to release criminal defendants before trial. Policymakers assert that providing public servants with algorithmic advice will improve human risk predictions and thereby lead to better (e.g., fairer) decisions. Yet because many policy decisions require balancing risk-reduction with competing goals, improving the accuracy of predictions may not necessarily improve the quality of decisions. If risk assessments make people more attentive to reducing risk at the expense of other values, these algorithms would diminish the implementation of public policy even as they lead to more accurate predictions. Through an experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first direct evidence that risk assessments can systematically alter how people factor risk into their decisions. These shifts counteracted the potential benefits of improved prediction accuracy. In the pretrial setting of our experiment, the risk assessment made participants more sensitive to increases in perceived risk; this shift increased the racial disparity in pretrial detention by 1.9%. In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8.3%. These results demonstrate the potential limits and harms of attempts to improve public policy by incorporating predictive algorithms into multifaceted policy decisions. If these observed behaviors occur in practice, presenting risk assessments to public servants would generate unexpected and unjust shifts in public policy without being subject to democratic deliberation or oversight.
Ensemble Squared: A Meta AutoML System
Venue: arXiv cs.LG 2021
Year: 2021

There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an off-the-shelf system can take care of many of the steps for end-users without the need for expertise in machine learning. This paper presents Ensemble Squared (Ensemble$^2$), an AutoML system that ensembles the results of state-of-the-art open-source AutoML systems. Ensemble$^2$ exploits the diversity of existing AutoML systems by leveraging the differences in their model search space and heuristics. Empirically, we show that diversity of each AutoML system is sufficient to justify ensembling at the AutoML system level. In demonstrating this, we also establish new state-of-the-art AutoML results on the OpenML tabular classification benchmark.
SSD-SSD: Communication sparsification for distributed deep learning training
Venue: arXiv cs.DC 2021
Year: 2021

Intensive communication and synchronization cost for gradients and parameters is the well-known bottleneck of distributed deep learning training. Based on the observations that Synchronous SGD (SSGD) obtains good convergence accuracy while asynchronous SGD (ASGD) delivers a faster raw training speed, we propose Several Steps Delay SGD (SSD-SGD) to combine their merits, aiming at tackling the communication bottleneck via communication sparsification. SSD-SGD explores both global synchronous updates in the parameter servers and asynchronous local updates in the workers in each periodic iteration. The periodic and flexible synchronization makes SSD-SGD achieve good convergence accuracy and fast training speed. To the best of our knowledge, we strike the new balance between synchronization quality and communication sparsification, and improve the trade-off between accuracy and training speed. Specifically, the core components of SSD-SGD include proper warm-up stage, steps delay stage, and our novel algorithm of global gradient for local update (GLU). GLU is critical for local update operations to effectively compensate the delayed local weights. Furthermore, we implement SSD-SGD on MXNet framework and comprehensively evaluate its performance with CIFAR-10 and ImageNet datasets. Experimental results show that SSD-SGD can accelerate distributed training speed under different experimental configurations, by up to 110%, while achieving good convergence accuracy.
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning
Venue: arXiv cs.RO 2021
Year: 2021

Vision-and-language navigation (VLN) is a challenging task that requires an agent to navigate in real-world environments by understanding natural language instructions and visual information received in real-time. Prior works have implemented VLN tasks on continuous environments or physical robots, all of which use a fixed camera configuration due to the limitations of datasets, such as 1.5 meters height, 90 degrees horizontal field of view (HFOV), etc. However, real-life robots with different purposes have multiple camera configurations, and the huge gap in visual information makes it difficult to directly transfer the learned navigation model between various robots. In this paper, we propose a visual perception generalization strategy based on meta-learning, which enables the agent to fast adapt to a new camera configuration with a few shots. In the training phase, we first locate the generalization problem to the visual perception module, and then compare two meta-learning algorithms for better generalization in seen and unseen environments. One of them uses the Model-Agnostic Meta-Learning (MAML) algorithm that requires a few shot adaptation, and the other refers to a metric-based meta-learning method with a feature-wise affine transformation layer. The experiment results show that our strategy successfully adapts the learned navigation model to a new camera configuration, and the two algorithms show their advantages in seen and unseen environments respectively.
The Representation Power of Neural Networks: Breaking the Curse of Dimensionality
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, breaking the curse of dimensionality. Our bounds hold for general activation functions, including ReLU. We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators.
Causal BERT : Language models for causality detection between events expressed in text
Venue: arXiv cs.CL 2021
Year: 2021

Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance. On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like Twitter, dedicated to communicating and exploring various types of causality in the real world. Recognizing these "Cause-Effect" relationships between natural language events continues to remain a challenge simply because it is often expressed implicitly. Implicit causality is hard to detect through most of the techniques employed in literature and can also, at times be perceived as ambiguous or vague. Also, although well-known datasets do exist for this problem, the examples in them are limited in the range and complexity of the causal relationships they depict especially when related to implicit relationships. Most of the contemporary methods are either based on lexico-semantic pattern matching or are feature-driven supervised methods. Therefore, as expected these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships and are hard to generalize. In this paper, we investigate the language model's capabilities for causal association among events expressed in natural language text using sentence context combined with event information, and by leveraging masked event context with in-domain and out-of-domain data distribution. Our proposed methods achieve the state-of-art performance in three different data distributions and can be leveraged for extraction of a causal diagram and/or building a chain of events from unstructured text.
Categorical Perception: A Groundwork for Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

A well-known perceptual consequence of categorization in humans and other animals, called categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities, and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, i.e. on how this geometry reflects the structure of the categories.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
Venue: arXiv cs.CV 2021
Year: 2021

Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal algorithms, for example, the alternating direction method of multipliers (ADMM), with advanced denoising priors. Over the past few years, great empirical success has been obtained by PnP algorithms, especially for the ones that integrate deep learning-based denoisers. However, a key challenge of PnP approaches is the need for manual parameter tweaking as it is essential to obtain high-quality results across the high discrepancy in imaging conditions and varying scene content. In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically. A core part of our approach is a policy network for automated parameter search which can be effectively learned via a mixture of model-free and model-based deep reinforcement learning strategies. We demonstrate, through rigorous numerical and visual experiments, that the learned policy can customize parameters to different settings, and is often more efficient and effective than existing handcrafted criteria. Moreover, we discuss several practical considerations of PnP denoisers, which together with our learned policy yield state-of-the-art results. This advanced performance is prevalent on both linear and nonlinear exemplar inverse imaging problems, and in particular shows promising results on compressed sensing MRI, sparse-view CT, single-photon imaging, and phase retrieval.
Data Privacy in Trigger-Action Systems
Venue: arXiv cs.CR 2021
Year: 2021

Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users' private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.
Portrait Neural Radiance Fields from a Single Image
Venue: arXiv cs.CV 2021
Year: 2021

We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts.
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization
Venue: arXiv cs.LG 2021
Year: 2021

Abstracting neural networks with constraints they impose on their inputs and outputs can be very useful in the analysis of neural network classifiers and to derive optimization-based algorithms for certification of stability and robustness of feedback systems involving neural networks. In this paper, we propose a convex program, in the form of a Linear Matrix Inequality (LMI), to certify incremental quadratic constraints on the map of neural networks over a region of interest. These certificates can capture several useful properties such as (local) Lipschitz continuity, one-sided Lipschitz continuity, invertibility, and contraction. We illustrate the utility of our approach in two different settings. First, we develop a semidefinite program to compute guaranteed and sharp upper bounds on the local Lipschitz constant of neural networks and illustrate the results on random networks as well as networks trained on MNIST. Second, we consider a linear time-invariant system in feedback with an approximate model predictive controller parameterized by a neural network. We then turn the stability analysis into a semidefinite feasibility program and estimate an ellipsoidal invariant set for the closed-loop system.
Towards Neural Programming Interfaces
Venue: arXiv cs.CL 2021
Year: 2021

It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, just as Application Programming Interfaces (APIs) control the behavior of programs by altering hyperparameters. In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained model to produce desired outputs. Importantly, no permanent changes are made to the weights of the original model, allowing us to re-purpose pretrained models for new tasks without overwriting any aspect of the language model. We also contribute a new data set construction algorithm and GAN-inspired loss function that allows us to train NPI models to control outputs of autoregressive transformers. In experiments against other state-of-the-art approaches, we demonstrate the efficacy of our methods using OpenAI's GPT-2 model, successfully controlling noun selection, topic aversion, offensive speech filtering, and other aspects of language while largely maintaining the controlled model's fluency under deterministic settings.
FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
Venue: arXiv cs.CR 2021
Year: 2021

The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business for attackers using botnets. Current centralized mitigation solutions concentrate on detection and mitigation at a victim's side, paying inadequate attention to hacking costs and the collaboration of defenders. Thus, we propose the federated learning empowered mitigation architecture (FLEAM) to advocate joint defense, incurring a higher hacking expense. FLEAM combines FL and fog computing to reduce mitigation time and improve detection accuracy, enabling defenders to jointly combatting botnets. Our comprehensive evaluations showcase that the attacking expense incurred is 2.5 times higher, the mitigation delay is about 72% lower, and the accuracy is 47% greater on average than classic solutions.
Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches
Venue: arXiv cs.AI 2021
Year: 2021

The role of verbal and non-verbal cues towards great public speaking has been a topic of exploration for many decades. We identify a commonality across present theories, the element of "variety or heterogeneity" in channels or modes of communication (e.g. resorting to stories, scientific facts, emotional connections, facial expressions etc.) which is essential for effectively communicating information. We use this observation to formalize a novel HEterogeneity Metric, HEM, that quantifies the quality of a talk both in the verbal and non-verbal domain (transcript and facial gestures). We use TED talks as an input repository of public speeches because it consists of speakers from a diverse community besides having a wide outreach. We show that there is an interesting relationship between HEM and the ratings of TED talks given to speakers by viewers. It emphasizes that HEM inherently and successfully represents the quality of a talk based on "variety or heterogeneity". Further, we also discover that HEM successfully captures the prevalent bias in ratings with respect to race and gender, that we call sensitive attributes (because prediction based on these might result in unfair outcome). We incorporate the HEM metric into the loss function of a neural network with the goal to reduce unfairness in rating predictions with respect to race and gender. Our results show that the modified loss function improves fairness in prediction without considerably affecting prediction accuracy of the neural network. Our work ties together a novel metric for public speeches in both verbal and non-verbal domain with the computational power of a neural network to design a fair prediction system for speakers.
6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment
Venue: arXiv cs.OH 2021
Year: 2021

Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a reduction of testing efforts by specifying safety relevant scenarios. To define those scenarios and operate in a complex real-world design domain, a structured description of the environment is needed. Within the PEGASUS research project, the 6-Layer Model (6LM) was introduced for the description of highway scenarios. This paper refines the 6LM and extends it to urban traffic and environment. As defined in PEGASUS, the 6LM provides the possibility to categorize the environment and, therefore, functions as a structured basis for subsequent scenario description. The model enables a structured description and categorization of the general environment, without incorporating any knowledge or anticipating any functions of actors. Beyond that, there is a variety of other applications of the 6LM, which are elaborated in this paper. The 6LM includes a description of the road network and traffic guidance objects, roadside structures, temporary modifications of the former, dynamic objects, environmental conditions and digital information. The work at hand specifies each layer by categorizing its items. Guidelines are formulated and explanatory examples are given to standardize the application of the model for an objective environment description. In contrast to previous publications, the model and its design are described in far more detail. Finally, the holistic description of the 6LM presented includes remarks on possible future work when expanding the concept to machine perception aspects.
An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data
Venue: arXiv cs.LG 2021
Year: 2021

Topology optimization is widely used by engineers during the initial product development process to get a first possible geometry design. The state-of-the-art is the iterative calculation, which requires both time and computational power. Some newly developed methods use artificial intelligence to accelerate the topology optimization. These require conventionally pre-optimized data and therefore are dependent on the quality and number of available data. This paper proposes an AI-assisted design method for topology optimization, which does not require pre-optimized data. The designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling (the volume percentage filled by material) as input data. In the training phase, geometries generated on the basis of random input data are evaluated with respect to given criteria. The results of those evaluations flow into an objective function which is minimized by adapting the predictor's parameters. After the training is completed, the presented AI-assisted design procedure supplies geometries which are similar to the ones generated by conventional topology optimizers, but requires a small fraction of the computational effort required by those algorithms. We anticipate our paper to be a starting point for AI-based methods that requires data, that is hard to compute or not available.