Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS has a number of characteristic artifacts that limit its usefulness in general-purpose applications. In this paper, we make progress toward understanding the behavior of SDS and its variants by viewing them as solving an optimal-cost transport path from a source distribution to a target distribution. Under this new interpretation, these methods seek to transport corrupted images (source) to the natural image distribution (target). We argue that current methods' characteristic artifacts are caused by (1) linear approximation of the optimal path and (2) poor estimates of the source distribution. We show that calibrating the text conditioning of the source distribution can produce high-quality generation and translation results with little extra overhead. Our method can be easily applied across many domains, matching or beating the performance of specialized methods. We demonstrate its utility in text-to-2D, text-based NeRF optimization, translating paintings to real images, optical illusion generation, and 3D sketch-to-real. We compare our method to existing approaches for score distillation sampling and show that it can produce high-frequency details with realistic colors.

In other words, if the input f(t) is composed of discrete events (as in the top panel of Figure 1), the memory representation of a particular event stored in f becomes more "fuzzy" as the time elapses. After enough time has elapsed, the events that were presented close in time will gradually blend together, as illustrated in the bottom panel of Figure 1. The top panel shows the one dimensional input signal consisting of a long, then short, then long pulse, with the activity of f shown at three different points in time in the panels below. This allows each SITH layer access to the entire compressed history at every time step without having to learn how long to maintain information from the past. A fifth parameter, dt, is the rate at which the input is "presented" to the network, which we set this value to 1 in all our experiments to indicate the input signal is being presented at the rate of 1 Hz. Figure 1: SITH layer compresses history leading up to the present Top A signal featuring a long, then short, then long pulse, separated by moments of no activation is the input to a SITH layer.

The Google I/O keynote took place earlier this week, and the company took the stage to unveil new features across all of its product offerings. This included AI upgrades to the Google Workspace suite of applications, which millions of users rely on every day to get their work done, including Google Docs, Meet, Slides, Gmail, and Vids. Also: Google's popular AI tool gets its own Android app - how to use NotebookLM on your phone The features unveiled this year focused on practicality. They embed AI features into the Google apps you already use every day to speed up your daily workflow by performing tedious and time-consuming tasks, such as cleaning out your inbox. Everyone can relate to being bombarded with emails.

Since their initial introduction, score-based diffusion models (SDMs) have been successfully applied to solve a variety of linear inverse problems in finite-dimensional vector spaces due to their ability to efficiently approximate the posterior distribution. However, using SDMs for inverse problems in infinite-dimensional function spaces has only been addressed recently, primarily through methods that learn the unconditional score. While this approach is advantageous for some inverse problems, it is mostly heuristic and involves numerous computationally costly forward operator evaluations during posterior sampling. To address these limitations, we propose a theoretically grounded method for sampling from the posterior of infinite-dimensional Bayesian linear inverse problems based on amortized conditional SDMs. In particular, we prove that one of the most successful approaches for estimating the conditional score in finite dimensions--the conditional denoising estimator--can also be applied in infinite dimensions. A significant part of our analysis is dedicated to demonstrating that extending infinite-dimensional SDMs to the conditional setting requires careful consideration, as the conditional score typically blows up for small times, contrarily to the unconditional score. We conclude by presenting stylized and large-scale numerical examples that validate our approach, offer additional insights, and demonstrate that our method enables large-scale, discretization-invariant Bayesian inference.

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks. These word representations are conditioned on their context within a sentence, and can also be used to encode the entire sentence. In this paper, we analyze the extent to which state-of-the-art models for contextual word representations, such as BERT and GPT-2, encode biases with respect to gender, race, and intersectional identities. Towards this, we propose assessing bias at the contextual word level. This novel approach captures the contextual effects of bias missing in context-free word embeddings, yet avoids confounding effects that underestimate bias at the sentence encoding level. We demonstrate evidence of bias at the corpus level, find varying evidence of bias in embedding association tests, show in particular that racial bias is strongly encoded in contextual word models, and observe that bias effects for intersectional minorities are exacerbated beyond their constituent minority identities. Further, evaluating bias effects at the contextual word level captures biases that are not captured at the sentence level, confirming the need for our novel approach.

The last two cases correspond to cost function gradients perpendicular to the faces of the feasible region. In linear optimization, these cases give is an infinite number of optimal solutions between two adjacent vertices. B.1 Baseline Algorithms We provide a brief discussion of the baseline algorithms below. The libraries our implementations are based off for PPO, SAC, and DreamerV2 are available under the MIT License, and the base MPO implementation under the Apache License 2.0. For MuJoCo [52], we used a Pro Lab license.

Unlike the prior results on fast spherical harmonic transform, our proposed algorithm works efficiently using a nearly optimal number of samples in any dimension d. Furthermore, we illustrate the empirical performance of our algorithm on numerical examples.

Adversarial reprogramming, introduced by Elsayed, Goodfellow, and Sohl-Dickstein, seeks to repurpose a neural network to perform a different task, by manipulating its input without modifying its weights. We prove that two-layer ReLU neural networks with random weights can be adversarially reprogrammed to achieve arbitrarily high accuracy on Bernoulli data models over hypercube vertices, provided the network width is no greater than its input dimension. We also substantially strengthen a recent result of Phuong and Lampert on directional convergence of gradient flow, and obtain as a corollary that training two-layer ReLU neural networks on orthogonally separable datasets can cause their adversarial reprogramming to fail. We support these theoretical results by experiments that demonstrate that, as long as batch normalisation layers are suitably initialised, even untrained networks with random weights are susceptible to adversarial reprogramming. This is in contrast to observations in several recent works that suggested that adversarial reprogramming is not possible for untrained networks to any degree of reliability.