62 research outputs found
Machine Learning Models that Remember Too Much
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data
Predictive coding with spiking neurons and feedforward gist signaling
Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature
Pragmatic Communication in Multi-Agent Collaborative Perception
Collaborative perception allows each agent to enhance its perceptual
abilities by exchanging messages with others. It inherently results in a
trade-off between perception ability and communication costs. Previous works
transmit complete full-frame high-dimensional feature maps among agents,
resulting in substantial communication costs. To promote communication
efficiency, we propose only transmitting the information needed for the
collaborator's downstream task. This pragmatic communication strategy focuses
on three key aspects: i) pragmatic message selection, which selects
task-critical parts from the complete data, resulting in spatially and
temporally sparse feature vectors; ii) pragmatic message representation, which
achieves pragmatic approximation of high-dimensional feature vectors with a
task-adaptive dictionary, enabling communicating with integer indices; iii)
pragmatic collaborator selection, which identifies beneficial collaborators,
pruning unnecessary communication links. Following this strategy, we first
formulate a mathematical optimization framework for the
perception-communication trade-off and then propose PragComm, a multi-agent
collaborative perception system with two key components: i) single-agent
detection and tracking and ii) pragmatic collaboration. The proposed PragComm
promotes pragmatic communication and adapts to a wide range of communication
conditions. We evaluate PragComm for both collaborative 3D object detection and
tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and
V2X-SIM2.0. PragComm consistently outperforms previous methods with more than
32.7K times lower communication volume on OPV2V. Code is available at
github.com/PhyllisH/PragComm.Comment: 18 page
First-order Convex Optimization Methods for Signal and Image Processing
In this thesis we investigate the use of first-order convex optimization methods applied to problems in signal and image processing. First we make a general introduction to convex optimization, first-order methods and their iteration com-plexity. Then we look at different techniques, which can be used with first-order methods such as smoothing, Lagrange multipliers and proximal gradient meth-ods. We continue by presenting different applications of convex optimization and notable convex formulations with an emphasis on inverse problems and sparse signal processing. We also describe the multiple-description problem. We finally present the contributions of the thesis. The remaining parts of the thesis consist of five research papers. The first paper addresses non-smooth first-order convex optimization and the trade-off between accuracy and smoothness of the approximating smooth function. The second and third papers concern discrete linear inverse problems and reliable numerical reconstruction software. The last two papers present a convex opti-mization formulation of the multiple-description problem and a method to solve it in the case of large-scale instances. i i
Neural Image Compression: Generalization, Robustness, and Spectral Biases
Recent advances in neural image compression (NIC) have produced models that
are starting to outperform classic codecs. While this has led to growing
excitement about using NIC in real-world applications, the successful adoption
of any machine learning system in the wild requires it to generalize (and be
robust) to unseen distribution shifts at deployment. Unfortunately, current
research lacks comprehensive datasets and informative tools to evaluate and
understand NIC performance in real-world settings. To bridge this crucial gap,
first, this paper presents a comprehensive benchmark suite to evaluate the
out-of-distribution (OOD) performance of image compression methods.
Specifically, we provide CLIC-C and Kodak-C by introducing 15 corruptions to
the popular CLIC and Kodak benchmarks. Next, we propose spectrally-inspired
inspection tools to gain deeper insight into errors introduced by image
compression methods as well as their OOD performance. We then carry out a
detailed performance comparison of several classic codecs and NIC variants,
revealing intriguing findings that challenge our current understanding of the
strengths and limitations of NIC. Finally, we corroborate our empirical
findings with theoretical analysis, providing an in-depth view of the OOD
performance of NIC and its dependence on the spectral properties of the data.
Our benchmarks, spectral inspection tools, and findings provide a crucial
bridge to the real-world adoption of NIC. We hope that our work will propel
future efforts in designing robust and generalizable NIC methods. Code and data
will be made available at https://github.com/klieberman/ood_nic.Comment: NeurIPS 202
Predictive coding with spiking neurons and feedforward gist signaling
Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature
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