23 research outputs found
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Most real-world applications that employ deep neural networks (DNNs) quantize
them to low precision to reduce the compute needs. We present a method to
improve the robustness of quantized DNNs to white-box adversarial attacks. We
first tackle the limitation of deterministic quantization to fixed ``bins'' by
introducing a differentiable Stochastic Quantizer (SQ). We explore the
hypothesis that different quantizations may collectively be more robust than
each quantized DNN. We formulate a training objective to encourage different
quantized DNNs to learn different representations of the input image. The
training objective captures diversity and accuracy via mutual information
between ensemble members. Through experimentation, we demonstrate substantial
improvement in robustness against attacks even if the attacker is
allowed to backpropagate through SQ (e.g., > 50\% accuracy to PGD(5/255) on
CIFAR10 without adversarial training), compared to vanilla DNNs as well as
existing ensembles of quantized DNNs. We extend the method to detect attacks
and generate robustness profiles in the adversarial information plane (AIP),
towards a unified analysis of different threat models by correlating the MI and
accuracy.Comment: 9 pages, 9 figures, 4 table
Recommended from our members
Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces
Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research has focused on exact and approximate solutions to MDPs with factored states, assuming a small number of actions. In contrast to this, many applications are most naturally modeled as having factored actions described in terms of multiple action variables. In this thesis we study domain-independent algorithms that leverage the factored action structure in the MDP dynamics and reward, and scale better than treating each of the exponentially many joint actions as atomic. Our contributions are three-fold based on three fundamental approaches to MDP planning namely exact solution using symbolic dynamic programming (DP), anytime online planning using heuristic search and online action selection using hindsight optimization.
The first part is focused on deriving optimal policies over all states for MDPs whose state and action space are described in terms of multiple discrete random variables. In order to capture the factored action structure, we introduce new symbolic operators for computing DP updates over all states
efficiently by leveraging the abstract and symbolic representation of Decision Diagrams. Addressing the potential bottleneck of diagrammatic blowup in these operators we present a novel
and optimal policy iteration algorithm that emphasizes the diagrammatic compactness of the intermediate value functions and policies. The impact is seen in experiments on the well-studied problems of inventory control and system administration where our algorithm is able to exploit the increasing compactness of the optimal policy with increasing complexity of the action space.
Under the framework of anytime planning, the second part expands the scalability of our approach to factored actions by restricting its attention to the reachable part of the state space. Our contribution is the introduction of new symbolic generalization operators that guarantee a more moderate use of space and time while providing non-trivial generalization. These operators yield anytime algorithms that guarantee convergence to the optimal value and action for the current world state, while guaranteeing bounded growth in the size of the symbolic representation. We empirically show that our online algorithm is successfully able to combine forward search from an initial state with backwards generalized DP updates on symbolic states.
The third part considers a general class of hybrid (mixed discrete and continuous) state and action (HSA) MDPs. Whereas the insights from the above approaches are valid for hybrid MDPs as well, there are significant limitations inherent to the DP approach. Existing solvers for hybrid state and action MDPs are either limited to very restricted transition distributions, require knowledge of domain-specific basis functions to achieve good approximations, or do not scale. We explore a domain-independent approach based on the framework of hindsight optimization (HOP) for HSA-MDPs, which uses an upper bound on the finite-horizon action values for action selection. Our main contribution is a linear time reduction to a Mixed Integer Linear Program (MILP) that encodes the HOP objective, when the dynamics are specified as location-scale probability distributions parametrized by Piecewise Linear (PWL) functions of states and actions. In addition, we show how to use the same machinery to select actions based on a lower-bound generated by straight-line plans. Our empirical results show that the HSA-HOP approach effectively scales to high-dimensional problems and outperforms baselines that are capable of scaling to such large hybrid MDPs. In a concluding case study, we cast the real-time dispatch optimization problem faced by the Corvallis Fire Department as an HSA-MDP with factored actions. We show that our domain-independent planner significantly improves upon the responsiveness of the baseline that dispatches the nearest responders
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
We present Samanantar, the largest publicly available parallel corpora
collection for Indic languages. The collection contains a total of 49.7 million
sentence pairs between English and 11 Indic languages (from two language
families). Specifically, we compile 12.4 million sentence pairs from existing,
publicly-available parallel corpora, and additionally mine 37.4 million
sentence pairs from the web, resulting in a 4x increase. We mine the parallel
sentences from the web by combining many corpora, tools, and methods: (a)
web-crawled monolingual corpora, (b) document OCR for extracting sentences from
scanned documents, (c) multilingual representation models for aligning
sentences, and (d) approximate nearest neighbor search for searching in a large
collection of sentences. Human evaluation of samples from the newly mined
corpora validate the high quality of the parallel sentences across 11
languages. Further, we extract 83.4 million sentence pairs between all 55 Indic
language pairs from the English-centric parallel corpus using English as the
pivot language. We trained multilingual NMT models spanning all these languages
on Samanantar, which outperform existing models and baselines on publicly
available benchmarks, such as FLORES, establishing the utility of Samanantar.
Our data and models are available publicly at
https://indicnlp.ai4bharat.org/samanantar/ and we hope they will help advance
research in NMT and multilingual NLP for Indic languages.Comment: Accepted to the Transactions of the Association for Computational
Linguistics (TACL
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
As Artificial and Robotic Systems are increasingly deployed and relied upon
for real-world applications, it is important that they exhibit the ability to
continually learn and adapt in dynamically-changing environments, becoming
Lifelong Learning Machines. Continual/lifelong learning (LL) involves
minimizing catastrophic forgetting of old tasks while maximizing a model's
capability to learn new tasks. This paper addresses the challenging lifelong
reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in
L2RL and making L2RL useful for practical applications requires more than
developing individual L2RL algorithms; it requires making progress at the
systems-level, especially research into the non-trivial problem of how to
integrate multiple L2RL algorithms into a common framework. In this paper, we
introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF),
which standardizes L2RL systems and assimilates different continual learning
components (each addressing different aspects of the lifelong learning problem)
into a unified system. As an instantiation of L2RLCF, we develop a standard API
allowing easy integration of novel lifelong learning components. We describe a
case study that demonstrates how multiple independently-developed LL components
can be integrated into a single realized system. We also introduce an
evaluation environment in order to measure the effect of combining various
system components. Our evaluation environment employs different LL scenarios
(sequences of tasks) consisting of Starcraft-2 minigames and allows for the
fair, comprehensive, and quantitative comparison of different combinations of
components within a challenging common evaluation environment.Comment: The Second International Conference on AIML Systems, October 12--15,
2022, Bangalore, Indi
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years,
state-of-the-art systems lack robustness to "real world" events, where the
input distributions and tasks encountered by the deployed systems will not be
limited to the original training context, and systems will instead need to
adapt to novel distributions and tasks while deployed. This critical gap may be
addressed through the development of "Lifelong Learning" systems that are
capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3)
Scalability. Unfortunately, efforts to improve these capabilities are typically
treated as distinct areas of research that are assessed independently, without
regard to the impact of each separate capability on other aspects of the
system. We instead propose a holistic approach, using a suite of metrics and an
evaluation framework to assess Lifelong Learning in a principled way that is
agnostic to specific domains or system techniques. Through five case studies,
we show that this suite of metrics can inform the development of varied and
complex Lifelong Learning systems. We highlight how the proposed suite of
metrics quantifies performance trade-offs present during Lifelong Learning
system development - both the widely discussed Stability-Plasticity dilemma and
the newly proposed relationship between Sample Efficient and Robust Learning.
Further, we make recommendations for the formulation and use of metrics to
guide the continuing development of Lifelong Learning systems and assess their
progress in the future.Comment: To appear in Neural Network