Hungryroot is a delivery meal plan that wants to know all about your life goals. Would I like to save time, money, or both? Do I have a problem with eggplants? Also, am I more of a candy person or a chocolate person, and what are my feelings about "spiced" versus "spicy" food? On the one hand, Hungryroot works much the same way as other meal delivery kits.

In this section we describe the denoising architectures used for our computational experiments. All architectures except BFCNN have additive (bias) terms after every convolutional layer. DnCNN [66] consists of 20 convolutional layers, each consisting of 3 3 filters and 64 channels, batch normalization [23], and a ReLU nonlinearity. It has a skip connection from the initial layer to the final layer, which has no nonlinear units. We use BFCNN [37] based on DnCNN architecture, i.e, we remove all sources of additive bias, including the mean parameter of the batch-normalization in every layer (note however that the scaling parameter is preserved). Our UNet model [50] has the following layers: 1. conv1 - Takes in input image and maps to 32 channels with 5 5 convolutional kernels. The input to this layer is the concatenation of the outputs of layer conv7 and conv2. The structure is the same as in [68]. This configuration of UNet assumes even width and height, so we remove one row or column from images in with odd height or width. We use a modified version of the blind-spot network architecture introduced in Ref. [29]. We rotate the input frames by multiples of 90 and process them through four separate branches (with shared weights) containing asymmetric convolutional filters that are vertically causal. The architecture of a branch is described in Table 1.

Diffusion Transformers (DiT) have attracted significant attention in research. However, they suffer from a slow convergence rate. In this paper, we aim to accelerate DiT training without any architectural modification. We identify the following issues in the training process: firstly, certain training strategies do not consistently perform well across different data. Secondly, the effectiveness of supervision at specific timesteps is limited. In response, we propose the following contributions: (1) We introduce a new perspective for interpreting the failure of the strategies. Specifically, we slightly extend the definition of Signal-to-Noise Ratio (SNR) and suggest observing the Probability Density Function (PDF) of SNR to understand the essence of the data robustness of the strategy.

Introspection was formalized by (51) as a field in psychology to understand the concepts of memory, feeling, and volition (52). The primary focus of introspection is in reflecting on oneself through directed questions. While the directed questions are an open field of study in psychology, we use reasoning as a means of questions in this paper. Abductive reasoning was introduced by the philosopher Charles Sanders Peirce (53), who saw abduction as a reasoning process from effect to cause (54). An abductive reasoning framework creates a hypothesis and tests its validity without considering the cause. From the perspective of introspection, a hypothesis can be considered as an answer to one of the three following questions: a correlation'Why P?' question, a counterfactual'What if?' question, and a contrastive'Why P, rather than Q?' question. Here P is the prediction and Q is any contrast class. Both the correlation and counterfactual questions require active interventions for answers. These questions try to assess the causality of some endogenous or exogenous variable and require interventions that are long, complex, and sometimes incomplete (55). However, introspection is the assessment of ones own notions rather than an external variable. Hence, a contrastive question of the form'Why P, rather than Q?' lends itself as the directed question for introspection. Here Q is the introspective class. It has the additional advantage that the network f() serves as the knowledge base of notions. All reflection images from 1, Figure 1, and Figure 1 are contrastive.

In this paper, we advocate for two stages in a neural network's decision making process. The first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. The second stage is a slower reflection stage where we ask the network to reflect on its feed-forward decision by considering and evaluating all available choices.

Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the policy evaluation steps, the critic is updated to capture the soft Q-function. In the policy improvement steps, the actor is adjusted in accordance with the updated soft Q-function. In this paper, we introduce a new MaxEnt RL framework modeled using Energy-Based Normalizing Flows (EBFlow).

Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data. However, while the bandits literature has adopted a diverse set of objectives, most research on off-policy evaluation to date focuses on the expected reward. In this paper, we introduce Lipschitz risk functionals, a broad class of objectives that subsumes conditional value-at-risk (CVaR), variance, mean-variance, many distorted risks, and CPT risks, among others. We propose Off-Policy Risk Assessment (OPRA), a framework that first estimates a target policy's CDF and then generates plugin estimates for any collection of Lipschitz risks, providing finite sample guarantees that hold simultaneously over the entire class.