16 research outputs found
Human alignment of neural network representations
Today's computer vision models achieve human or near-human level performance
across a wide variety of vision tasks. However, their architectures, data, and
learning algorithms differ in numerous ways from those that give rise to human
vision. In this paper, we investigate the factors that affect the alignment
between the representations learned by neural networks and human mental
representations inferred from behavioral responses. We find that model scale
and architecture have essentially no effect on the alignment with human
behavioral responses, whereas the training dataset and objective function both
have a much larger impact. These findings are consistent across three datasets
of human similarity judgments collected using two different tasks. Linear
transformations of neural network representations learned from behavioral
responses from one dataset substantially improve alignment with human
similarity judgments on the other two datasets. In addition, we find that some
human concepts such as food and animals are well-represented by neural networks
whereas others such as royal or sports-related objects are not. Overall,
although models trained on larger, more diverse datasets achieve better
alignment with humans than models trained on ImageNet alone, our results
indicate that scaling alone is unlikely to be sufficient to train neural
networks with conceptual representations that match those used by humans.Comment: Accepted for publication at ICLR 202
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning and
difficult to account for when applying standard empirical risk minimization. In
this work, we propose a novel method to alleviate these problems that we call
odd--out learning (OKO), which minimizes the cross-entropy error for sets
rather than for single examples. This naturally allows the model to capture
correlations across data examples and achieves both better accuracy and
calibration, especially in limited training data and class-imbalanced regimes.
Perhaps surprisingly, OKO often yields better calibration even when training
with hard labels and dropping any additional calibration parameter tuning, such
as temperature scaling. We provide theoretical justification, establishing that
OKO naturally yields better calibration, and provide extensive experimental
analyses that corroborate our theoretical findings. We emphasize that OKO is a
general framework that can be easily adapted to many settings and the trained
model can be applied to single examples at inference time, without introducing
significant run-time overhead or architecture changes
Improving neural network representations using human similarity judgments
Deep neural networks have reached human-level performance on many computer
vision tasks. However, the objectives used to train these networks enforce only
that similar images are embedded at similar locations in the representation
space, and do not directly constrain the global structure of the resulting
space. Here, we explore the impact of supervising this global structure by
linearly aligning it with human similarity judgments. We find that a naive
approach leads to large changes in local representational structure that harm
downstream performance. Thus, we propose a novel method that aligns the global
structure of representations while preserving their local structure. This
global-local transform considerably improves accuracy across a variety of
few-shot learning and anomaly detection tasks. Our results indicate that human
visual representations are globally organized in a way that facilitates
learning from few examples, and incorporating this global structure into neural
network representations improves performance on downstream tasks.Comment: Published as a conference paper at NeurIPS 202
Getting aligned on representational alignment
Biological and artificial information processing systems form representations
that they can use to categorize, reason, plan, navigate, and make decisions.
How can we measure the extent to which the representations formed by these
diverse systems agree? Do similarities in representations then translate into
similar behavior? How can a system's representations be modified to better
match those of another system? These questions pertaining to the study of
representational alignment are at the heart of some of the most active research
areas in cognitive science, neuroscience, and machine learning. For example,
cognitive scientists measure the representational alignment of multiple
individuals to identify shared cognitive priors, neuroscientists align fMRI
responses from multiple individuals into a shared representational space for
group-level analyses, and ML researchers distill knowledge from teacher models
into student models by increasing their alignment. Unfortunately, there is
limited knowledge transfer between research communities interested in
representational alignment, so progress in one field often ends up being
rediscovered independently in another. Thus, greater cross-field communication
would be advantageous. To improve communication between these fields, we
propose a unifying framework that can serve as a common language between
researchers studying representational alignment. We survey the literature from
all three fields and demonstrate how prior work fits into this framework.
Finally, we lay out open problems in representational alignment where progress
can benefit all three of these fields. We hope that our work can catalyze
cross-disciplinary collaboration and accelerate progress for all communities
studying and developing information processing systems. We note that this is a
working paper and encourage readers to reach out with their suggestions for
future revisions.Comment: Working paper, changes to be made in upcoming revision
Synthetic α-Conotoxin Mutants as Probes for Studying Nicotinic Acetylcholine Receptors and in the Development of Novel Drug Leads
α-Conotoxins are peptide neurotoxins isolated from venomous marine cone snails that are potent and selective antagonists for different subtypes of nicotinic acetylcholine receptors (nAChRs). As such, they are valuable probes for dissecting the role that nAChRs play in nervous system function. In recent years, extensive insight into the binding mechanisms of α-conotoxins with nAChRs at the molecular level has aided in the design of synthetic analogs with improved pharmacological properties. This review examines the structure-activity relationship studies involving α-conotoxins as research tools for studying nAChRs in the central and peripheral nervous systems and their use towards the development of novel therapeutics
Optimizing hydraulic reservoirs using euler-eulerlagrange multiphase cfd simulation
Well working hydraulic systems need clean hydraulic oil. Therefore, the system must ensure the separation of molecular, gaseous, liquid and solid contaminations. The key element of the separation of contaminants is the hydraulic reservoir. Solid particles are a major source of maintenance costs and machine downtime. Thus, an Euler-Euler-Lagrange multiphase CFD model to predict the transport of solid particles in hydraulic reservoirs was developed. The CFD model identifies and predicts the particle accumulation areas and is used to train port-to-port transfer functions, which can be used in system models to simulate the long-term contamination levels of hydraulic systems. The experimental detection of dynamic particle contamination levels and particle accumulation areas validate and confirm the CFD and the system model. Both models in combination allow for parameter and design studies to improve the fluid management of hydraulic reservoirs
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks
Over the past decade, deep neural network (DNN) models have received a lot of attention due to their near-human object classification performance and their excellent prediction of signals recorded from biological visual systems. To better understand the function of these networks and relate them to hypotheses about brain activity and behavior, researchers need to extract the activations to images across different DNN layers. The abundance of different DNN variants, however, can often be unwieldy, and the task of extracting DNN activations from different layers may be non-trivial and error-prone for someone without a strong computational background. Thus, researchers in the fields of cognitive science and computational neuroscience would benefit from a library or package that supports a user in the extraction task. THINGSvision is a new Python module that aims at closing this gap by providing a simple and unified tool for extracting layer activations for a wide range of pretrained and randomly-initialized neural network architectures, even for users with little to no programming experience. We demonstrate the general utility of THINGsvision by relating extracted DNN activations to a number of functional MRI and behavioral datasets using representational similarity analysis, which can be performed as an integral part of the toolbox. Together, THINGSvision enables researchers across diverse fields to extract features in a streamlined manner for their custom image dataset, thereby improving the ease of relating DNNs, brain activity, and behavior, and improving the reproducibility of findings in these research fields